integrative approach to literature review

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• v.26(5); 2021 Aug

Conducting integrative reviews: a guide for novice nursing researchers

Shannon dhollande.

Lecturer, School of Nursing, Midwifery & Social Sciences, CQ University Brisbane, Australia

Annabel Taylor

Professor, School of Nursing, Midwifery & Social Sciences, CQ University Brisbane, Australia

Silke Meyer

Associate Professor, School of Social Sciences, Monash University, Australia

Emergency Consultant, Emergency Department, Caboolture Hospital, Australia

Integrative reviews within healthcare promote a holistic understanding of the research topic. Structure and a comprehensive approach within reviews are important to ensure the reliability in their findings.

This paper aims to provide a framework for novice nursing researchers undertaking integrative reviews.

Established methods to form a research question, search literature, extract data, critically appraise extracted data and analyse review findings are discussed and exemplified using the authors’ own review as a comprehensive and reliable approach for the novice nursing researcher undertaking an integrative literature review.

Providing a comprehensive audit trail that details how an integrative literature review has been conducted increases and ensures the results are reproducible. The use of established tools to structure the various components of an integrative review increases robustness and readers’ confidence in the review findings.

Implications for practice

Novice nursing researchers may increase the reliability of their results by employing a framework to guide them through the process of conducting an integrative review.

A literature review is a critical analysis of published research literature based on a specified topic ( Pluye et al., 2016 ). Literature reviews identify literature then examine its strengths and weaknesses to determine gaps in knowledge ( Pluye et al. 2016 ). Literature reviews are an integral aspect of research projects; indeed, many reviews constitute a publication in themselves ( Snyder, 2019 ). There are various types of literature reviews based largely on the type of literature sourced ( Cronin et al. 2008 ). These include systematic literature reviews, traditional, narrative and integrative literature reviews ( Snyder, 2019 ). Aveyard and Bradbury-Jones (2019) found more than 35 commonly used terms to describe literature reviews. Within healthcare, systematic literature reviews initially gained traction and widespread support because of their reproducibility and focus on arriving at evidence-based conclusions that could influence practice and policy development ( Boell and Cecez-Kecmanovic, 2015 ). Yet, it became apparent that healthcare-related treatment options needed to review broader spectrums of research for treatment options to be considered comprehensive, holistic and patient orientated ( Boell and Cecez-Kecmanovic, 2015 ). Stern et al. (2014) suggest that despite the focus in healthcare on quantitative research not all pertinent questions surrounding the provision of care can be answered from this approach. To devise solutions to multidimensional problems, all forms of trustworthy evidence need to be considered ( Stern et al. 2014 ).

Integrative reviews assimilate research data from various research designs to reach conclusions that are comprehensive and reliable ( Soares et al. 2014 ). For example, an integrative review considers both qualitative and quantitative research to reach its conclusions. This approach promotes the development of a comprehensive understanding of the topic from a synthesis of all forms of available evidence ( Russell, 2005 ; Torraco, 2005 ). The strengths of an integrative review include its capacity to analyse research literature, evaluate the quality of the evidence, identify knowledge gaps, amalgamate research from various research designs, generate research questions and develop theoretical frameworks ( Russell, 2005 ). Aveyard and Bradbury-Jones (2019) suggested that integrative reviews exhibit similar characteristics to systematic reviews and may therefore be regarded as rigorous.

Integrative reviews value both qualitative and quantitative research which are built upon differing epistemological paradigms. Both types of research are vital in developing the evidence base that guides healthcare provision ( Leppäkoski and Paavilainen, 2012 ). Therefore, integrative reviews may influence policy development as their conclusions have considered a broad range of appropriate literature ( Whittemore and Knafl, 2005 ). An integrative approach to evidence synthesis allows healthcare professionals to make better use of all available evidence and apply it to the clinical practice environment ( Souza et al. 2010 ). For example, Aveyard and Bradbury-Jones (2019) found in excess of 12 different types of reviews employed to guide healthcare practice. The healthcare profession requires both quantitative and qualitative forms of research to establish the robust evidence base that enables the provision of evidence-based patient-orientated healthcare.

Integrative reviews require a specific set of skills to identify and synthesise literature ( Boell and Cecez-Kecmanovic, 2010 ). There remains a paucity of literature that provides explicit guidance to novice nursing researchers on how to conduct an integrative review and importantly how to ensure the results and conclusions are both comprehensive and reliable. Furthermore, novice nursing researchers may receive little formal training to develop the skills required to generate a comprehensive integrative review ( Boote and Beile, 2005 ). Aveyard and Bradbury-Jones (2019) also emphasised the limited literature providing guidance surrounding integrative reviews. Therefore, novice nursing researchers need to rely on published guidance to assist them. In this regard this paper, using an integrative review conducted by the authors as a case study, aims to provide a framework for novice nursing researchers conducting integrative reviews.

Developing the framework

In conducting integrative reviews, the novice nursing researcher may need to employ a framework to ensure the findings are comprehensive and reliable ( Boell and Cecez-Kecmanovic, 2010 ; Snyder, 2019 ). A framework to guide novice nursing researchers in conducting integrative reviews has been adapted by the authors and will now be described and delineated. This framework used various published literature to guide its creation, namely works by Aveyard and Bradbury-Jones (2019) , Nelson (2014), Stern et al. (2014) , Whittemore and Knafl (2005) , Pluye et al. (2009) , Moher et al., (2009) and Attride-Stirling, (2001) . The suggested framework involves seven steps ( Figure 1 ).

Integrative review framework ( Cooke et al. 2012 ; Riva et al. 2012 ).

Step 1: Write the review question

The review question acts as a foundation for an integrative study ( Riva et al. 2012 ). Yet, a review question may be difficult to articulate for the novice nursing researcher as it needs to consider multiple factors specifically, the population or sample, the interventions or area under investigation, the research design and outcomes and any benefit to the treatment ( Riva et al. 2012 ). A well-written review question aids the researcher to develop their research protocol/design and is of vital importance when writing an integrative review.

To articulate a review question there are numerous tools available to the novice nursing researcher to employ. These tools include variations on the PICOTs template (PICOT, PICO, PIO), and the Spider template. The PICOTs template is an established tool for structuring a research question. Yet, the SPIDER template has gained acceptance despite the need for further research to determine its applicability to multiple research contexts ( Cooke et al., 2012 ). Templates are recommended to aid the novice nursing researcher in effectively delineating and deconstructing the various elements within their review question. Delineation aids the researcher to refine the question and produce more targeted results within a literature search. In the case study, the review question was to: identify, evaluate and synthesise current knowledge and healthcare approaches to women presenting due to intimate partner violence (IPV) within emergency departments (ED). This review objective is delineated in the review question templates shown in Table 1 .

Comparison of elements involved with a PICOTS and SPIDER review question.

( Cooke et al. 2012 ; Riva et al. 2012 ).

Step 2: Determine the search strategy

In determining a search strategy, it is important for the novice nursing researcher to consider the databases employed, the search terms, the Boolean operators, the use of truncation and the use of subject headings. Furthermore, Nelson (2014) suggests that a detailed description of the search strategy should be included within integrative reviews to ensure readers are able to reproduce the results.

The databases employed within a search strategy need to consider the research aim and the scope of information contained within the database. Many databases vary in their coverage of specific journals and associated literature, such as conference proceedings ( Boell and Cecez-Kecmanovic, 2010 ). Therefore, the novice nursing researcher should consult several databases when conducting their searches. For example, search strategies within the healthcare field may utilise databases such as Cumulative Index to Nursing and Allied Healthcare Literature (CINAHL), Cochrane Library, Science Direct, ProQuest, Web of Science, Scopus and PsychInfo ( Cronin et al. 2008 ). These databases among others are largely considered appropriate repositories of reliable data that novice researchers may utilise when researching within healthcare. The date in which the searches are undertaken should be within the search strategy as searches undertaken after this date may generate increased results in line with the publication of further studies.

Utilising an established template to generate a research question allows for the delineation of key elements within the question as seen above. These key elements may assist the novice nursing researcher in determining the search terms they employ. Furthermore, keywords on published papers may provide the novice nursing researcher with alternative search terms, synonyms and introduce the researcher to key terminology employed within their field ( Boell and Cecez-Kecmanovic, 2010 ). For example, within the case study undertaken the search terms included among others: ‘domestic violence’, ‘domestic abuse’, ‘intimate partner violence and/or abuse’. To refine the search to the correct healthcare environment the terms ‘emergency department’ and/or ‘emergency room’ were employed. To link search terms, the researcher should consider their use of Boolean operators ‘And’ ‘Or’ and ‘Not’ and their use of truncation ( Cronin et al. 2008 ). Truncation is the shortening of words which in literature searches may increase the number of search results. Medical subject headings (MeSH) or general subject headings should be employed where appropriate and within this case study the headings included ‘nursing’, ‘domestic violence’ and ‘intimate partner violence’.

Inclusion and exclusion criteria allow the novice nursing researcher to reduce and refine the search parameters and locate the specific data they seek. Appropriate use of inclusion and exclusion criteria permits relevant data to be sourced as wider searches can produce a large amount of disparate data, whereas a search that is too narrow may result in the omission of significant findings ( Boell and Cecez-Kecmanovic, 2010 ). The novice nursing researcher needs to be aware that generating a large volume of search results may not necessarily result in relevant data being identified. Within integrative reviews there is potential for a large volume of data to be sourced and therefore time and resources required to complete the review need to be considered ( Heyvaert et al. 2017 ). The analysis and refining of a large volume of data can become a labour-intensive exercise for the novice nursing researcher ( Boell and Cecez-Kecmanovic, 2010 ).

Stern et al. (2014) suggest various elements that should be considered within inclusion/exclusion criteria:

• the type of studies included;
• the topic under exploration;
• the outcomes;
• publication language;
• the time period; and
• the methods employed.

The use of limiters or exclusion criteria are an effective method to manage the amount of time it takes to undertake searches and limit the volume of research generated. Yet, exclusion criteria may introduce biases in the search results and should therefore be used with caution and to produce specific outcomes by the novice nursing researcher ( Hammerstrøm et al. 2017 ).

Whittemore and Knafl (2005) suggest that randomised controlled trials, prospective and retrospective cohort studies, case control studies, cross sectional studies, systematic reviews and meta-analyses should all be included within the search strategy. Therefore, there are no biases based on the type of publication sourced ( Hammerstrøm et al. 2017 ).

There should be no restriction on the sample size within the studies recognising that qualitative studies generally have smaller sample sizes, and to capture the breadth of research available. There was no restriction on the date of publication within the case study as quality literature was limited. Scoping widely is an important strategy within integrative reviews to produce comprehensive results. A manual citation search of the reference list of all sourced papers was also undertaken by a member of the research team.

Literature may be excluded if those papers were published in a language foreign to the researcher with no accepted translation available. Though limiting papers based on translation availability may introduce some bias, this does ensure the review remains free from translational errors and cultural misinterpretations. In the case study, research conducted in developing countries with a markedly different healthcare service and significant resource limitations were excluded due to their lack of generalisability and clinical relevance; though this may have introduced a degree of location bias ( Nelson, 2014 ).

A peer review of the search strategy by an individual who specialises in research data searches such as a research librarian may be a viable method in which the novice healthcare researcher can ensure the search strategy is appropriate and able to generate the required data. One such tool that a novice nurse may employ is the Peer Review of the Search Strategy (PRESS) checklist. A peer review of the caste study was undertaken by a research librarian. All recommendations were incorporated into the search strategy which included removing a full text limiter, and changes to the Boolean and proximity operators.

After the search strategy has been implemented the researcher removes duplicate results and screened the retrieved publications based on their titles and abstracts. A second screening was then undertaken based on the full text of retrieved publications to remove papers that were irrelevant to the research question. Full text copies should then be obtained for critical appraisal employing validated methods.

Step 3: Critical appraisal of search results

The papers identified within the search strategy should undergo a critical appraisal to determine if they are appropriate and of sufficient quality to be included within the review. This should be conducted or reviewed by a second member of the research team, which occurred within this case study. Any discrepancies were discussed until consensus was achieved. A critical appraisal allows the novice healthcare researcher to appraise the relevance and trustworthiness of a study and, therefore, determine its applicability to their research (CASP, 2013). There are several established tools a novice nurse can employ in which to structure their critical appraisal. These include the Scoring System for Mixed-Methods Research and Mixed Studies Reviews developed by Pluye et al. (2009) and the Critical Appraisal Skills Programme (CASP, 2018) Checklists.

The review undertaken by the authors employed the scoring system for mixed-methods research and mixed-studies reviews developed by Pluye et al. (2009) . This scoring system was specifically designed for reviews employing studies from various research designs and therefore was utilised with ease ( Table 2 ).

The scoring system for mixed-methods research and mixed-studies reviews ( Pluye et al. 2009 ).

Using the CASP checklist aids the novice nursing researcher to examine the methodology of identified papers to establish validity. This critical appraisal tool contains 10 items. These items are yes or no questions that assist the researcher to determine (a) if the results of the paper are valid, (b) what the results are and (c) if it is relevant in the context of their study. For example, the checklist asks the researcher to consider the presence of a clear statement surrounding the aims of the research, and to consider why and how the research is important in regard to their topic (CASP, 2013). This checklist supports the nurse researcher to assess the validity, results and significance of research, and therefore appropriately decide on its inclusion within the review ( Krainovich-Miller et al., 2009 ).

Step 4: Summarise the search results

A summary of the results generated by literature searches is important to exemplify how comprehensive the literature is or conversely to identify if there are gaps in research. This summary should include the number of, and type of papers included within the review post limiters, screening and critical appraisal of search results. For example, within the review detailed throughout this paper the search strategy resulted in the inclusion of 25 qualitative and six quantitative papers ( Bakon et al. 2019 ). Many papers provide a summary of their search results visually in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram ( Boell and Cecez-Kecmanovic, 2015 ). PRISMA is a method of reporting that enables readers to assess the robustness of the results ( Leclercq et al. 2019 ; Moher et al. 2009 ). PRISMA promotes the transparency of the search process by delineating various items within the search process ( Leclercq et al. 2019 ; Moher et al. 2009 ). Researchers may decide how rigorously they follow this process yet should provide a rationale for any deviations ( Leclercq et al. 2019 ; Moher et al, 2009 ). Figure 2 is an example of the PRISMA flow diagram as it was applied within the case study.

Example PRISMA flow diagram ( Bakon et al. 2019 ; Moher et al. 2009 ).

Step 5: Data extraction and reduction

Data can be extracted from the critically appraised papers identified through the search strategy employing extraction tables. Within the case study data were clearly delineated, as suggested by Boell and Cecez-Kecmanovic (2010) , into extraction or comparison tables ( Table 3 ). These tables specify the authors, the date of publication, year of publication, site where the research was conducted and the key findings. Setting out the data into tables facilitates the comparison of these variables and aids the researcher to determine the appropriateness of the papers’ inclusion or exclusion within the review ( Whittemore and Knafl, 2005 ).

Example of a data extraction table.

Step 6: Analysis

Thematic analysis is widely used in integrative research ( Attride-Stirling, 2001 ). In this section we will discuss the benefits of employing a structured approach to thematic analysis including the formation of a thematic network. A thematic network is a visual diagram or depiction of the themes displaying their interconnectivity. Thematic analysis with the development of a thematic network is a way of identifying themes at various levels and depicting the observed relationships and organisation of these themes ( Attride-Stirling, 2001 ). There are numerous methods and tools available in which to conduct a thematic analysis that may be of use to the novice healthcare researcher conducting an integrative review. The approach used in a thematic analysis is important though a cursory glance at many literature reviews will reveal that many authors do not delineate the methods they employ. This includes the thematic analysis approach suggested by Thomas and Harden (2008) and the approach to thematic networking suggested by Attride-Stirling (2001) .

Thomas and Harden (2008) espouse a three-step approach to thematic analysis which includes: (a) coding, (b) organisation of codes into descriptive themes, and (c) the amalgamation of descriptive themes into analytical themes. The benefit of this approach lies in its simplicity and the ease with which a novice nurse researcher can apply the required steps. In contrast, the benefit of the approach suggested by Attride-Stirling (2001) lies in its ability to move beyond analysis and generate a visual thematic network which facilitates a critical interpretation and synthesis of the data.

Thematic networks typically depict three levels: basic themes, organising themes and global themes ( Attride-Stirling, 2001 ). The thematic network can then be developed. A thematic network is a visual depiction that appears graphically as a web like design ( Attride-Stirling, 2001 ). Thematic networks emphasise the relationships and interconnectivity of the network. It is an illustrative tool that facilitates interpretation of the data ( Attride-Stirling, 2001 ).

The benefits of employing a thematic analysis and networking within integrative reviews is the flexibility inherent within the approach, which allows the novice nursing researcher to provide a comprehensive accounting of the data ( Nowell et al. 2017 ). Thematic analysis is also an easily grasped form of data analysis that is useful for exploring various perspectives on specific topics and highlighting knowledge gaps ( Nowell et al. 2017 ). Thematic analysis and networking is also useful as a method to summarise large or diversified data sets to produce insightful conclusions ( Attride-Stirling, 2001 ; Nowell et al. 2017 ). The ability to assimilate data from various seemingly disparate perspectives may be challenging for the novice nursing researcher conducting an integrative review yet this integration of data by thematic analysis and networking was is integral.

To ensure the trustworthiness of results, novice nursing researchers need to clearly articulate each stage within the chosen method of data analysis ( Attride-Stirling, 2001 ; Nowell et al. 2017 ). The method employed in data analysis needs to be precise and exhaustively delineated ( Attride-Stirling, 2001 ; Nowell et al. 2017 ). Attride-Stirling (2001) suggests six steps within her methods of thematic analysis and networking. These steps include:

• code material;
• identify themes;
• construct thematic network;
• describe and explore the thematic network;
• summarise thematic network findings; and
• interpret patterns to identify implications.

In employing the approach suggested by Attride-Stirling (2001) within the case study the coding of specific findings within the data permitted the development of various themes ( Table 4 ). Inclusion of both quantitative and qualitative findings within the themes facilitated integration of the data which identified patterns and generated insights into the current care provided to IPV victims within ED.

Coding and theme formation.

Step 7: Conclusions and implications

A conclusion is important to remind the reader why the research topic is important. The researcher can then follow advice by Higginbottom (2015) who suggests that in drawing and writing research conclusions the researcher has an opportunity to explain the significance of the findings. The researcher may also need to explain these conclusions in light of the study limitations and parameters. Higginbottom (2015) emphasises that a conclusion is not a summary or reiteration of the results but a section which details the broader implications of the research and translates this knowledge into a format that is of use to the reader. The implications of the review findings for healthcare practice, for healthcare education and research should be considered.

Employing this structured and comprehensive framework within the case study the authors were able to determine that there remains a marked barrier in the provision of healthcare within the ED to women presenting with IPV-related injury. By employing an integrative approach multiple forms of literature were reviewed, and a considerable gap was identified. Therefore, further research may need to focus on the developing a structured healthcare protocol to aid ED clinicians to meet the needs of this vulnerable patient population.

Integrative reviews can be conducted with success when they follow a structured approach. This paper proposes a framework that novice nursing researchers can employ. Applying our stepped framework within an integrative review will strengthen the robustness of the study and facilitate its translation into policy and practice. This framework was employed by the authors to identify, evaluate and synthesise current knowledge and approaches of health professionals surrounding the care provision of women presenting due to IPV within emergency departments. The recommendations from the case study are currently being translated and implemented into the practice environment.

Key points for policy, practice and/or research

• Integrative literature reviews are required within nursing to consider elements of care provision from a holistic perspective.
• There is currently limited literature providing explicit guidance on how to undertake an integrative literature review.
• Clear delineation of the integrative literature review process demonstrates how the knowledge base was understood, organised and analysed.
• Nurse researchers may utilise this guidance to ensure the reliability of their integrative review.

Shannon Dhollande is a Lecturer, registered nurse and researcher. Her research explores the provision of emergency care to vulnerable populations.

Annabel Taylor is a Professorial Research Fellow at CQ University who with her background in social work explores methods of addressing gendered violence such as domestic violence.

Silke Meyer is an Associate Professor in Criminology and the Deputy Director of the Gender and Family Violence Prevention Centre at Monash University.

Mark Scott is an Emergency Medical Consultant with a track record in advancing emergency healthcare through implementation of evidence-based healthcare.

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics: Due to the nature of this article this article did not require ethical approval.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Shannon Dhollande https://orcid.org/0000-0003-3181-7606

Silke Meyer https://orcid.org/0000-0003-3964-042X

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• Integrative Reviews

What is an Integrative Review?

An  integrative review provides a broader summary of the literature and includes findings from a range of research designs. It gathers and synthesizes  both empirical and theoretical evidence  relevant to a clearly defined problem. It may include case studies, observational studies, and meta-analyses, but may also include practice applications, theory, and guidelines. It is the only approach that allows for the combination of diverse methodologies. Its aim is to develop a holistic understanding   of the topic, present the state of the science and contribute to theory development.  The integrative review has been advocated as important for evidence-based practice initiatives in nursing  (Hopia et al., 2016).

Integrative reviews are popular in nursing because they use diverse data sources to investigate the complexity of nursing practice. An integrative review addresses the current state of the evidence, the quality of the available evidence, identifies gaps in the literature and suggests future directions for research and practice The clinical question(s)   of an integrative review   is broader  than that of a systematic review, yet should be clearly stated and well-defined. As with a systematic review, an integrative review requires a transparent and rigorous systematic approach  (Toronto & Remington, 2020).

Integrative reviews synthesize research data from various research designs to reach comprehensive and reliable conclusions. An integrative review helps to develop a comprehensive understanding of the topic by synthesizing  all forms of available evidence (Dhollande et al., 2021). They allow healthcare professionals to use all available evidence from both  qualitative and quantitative research to provide a more holistic understanding of the topic, which can then be applied to clinical practice. Sampling for an integrative review may include experimental and nonexperimental (empirical) and theoretical literature (Toronto & Remington, 2020). 

Steps of the Integrative Review Process

1: Select a Topic:  Formulate a purpose and/or review question(s).   An integrative review can be used to answer research questions related to nursing and other disciplines.   Clearly identify a problem from a gap in the literature. Perform a quick search for other literature reviews related to the topic of interest to avoid duplication. Integrative review questions should be  broad in scope, but narrow enough that the search is manageable.  It should be  well-defined,  and  clearly stated . Provide background on the topic and justification for the integrative review. Do a quick literature search to determine if any recent integrative or other types of reviews on or related to the topic have been performed.

Quality Appraisal Tools for Integrative Reviews

Critical Appraisal Skills Programme (CASP) Checklists  Appraisal checklists designed for use with Systematic Reviews, Randomized Controlled Trials, Cohort Studies,  Case Control  Studies, Economic Evaluations, Diagnostic Studies, Qualitative studies and Clinical Prediction Rule.

Mixed Methods Appraisal Tool (MMAT)  A critical appraisal tool that was developed for use in systematic mixed studies reviews (i.e., reviews combining qualitative, quantitative and/or mixed methods studies) (Hong et al., 2018).

For more information on integrative reviews:

Dhollande, S., Taylor, A., Meyer, S., & Scott, M. (2021). Conducting integrative reviews: A guide for novice nursing researchers.  Journal of Research in Nursing, 26( 5), 427–438. https://doi.org/10.1177/1744987121997907

Evans, D. (2007). Integrative reviews: Overview of methods. In C. Webb, & B. Roe (Eds.),  Reviewing research evidence for nursing practice: Systematic reviews  (pp. 135 - 148). John Wiley & Sons, Incorporated.

Oermann, M. H., & Knafl, K. A. (2021). Strategies for completing a successful integrative review.  Nurse Author & Editor (Blackwell) ,  31 (3/4), 65–68. https://doi-org.libproxy.adelphi.edu/10.1111/nae2.30

Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology.  Journal of Advanced Nursing ,  52 (5), 546–553. https://doi.org/10.1111/j.1365-2648.2005.03621.x

Whittemore, R. (2007). Rigour in integrative reviews. In C. Webb, & B. Roe (Eds.),  Reviewing research evidence for nursing practice: Systematic reviews  (pp. 149 - 156). John Wiley & Sons, Incorporated.

References:

Daudt, H. M., van Mossel, C., & Scott, S. J. (2013).Enhancing the scoping study methodology: A large, inter-professional team's experience with Arksey and O'Malley's framework.  BMC Medical Research Methodology, 13 :48. https://doi.org/10.1186/1471-2288-13-48

Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M.-C., Vedel, I., & Pluye, P. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers.  Education for Information, 34 (4), 285–291. https://doi.org/10.3233/EFI-180221

Hopia, Latvala, E., & Liimatainen, L. (2016). Reviewing the methodology of an integrative review.  Scandinavian Journal of Caring Sciences,  30 (4), 662–669. https://doi.org/10.1111/scs.12327

Kutcher, & LeBaron, V. T. (2022). A simple guide for completing an integrative review using an example article.  Journal of Professional Nursing,  40 , 13-19. 

Toronto, C. E., & Remington, R. (Eds.). (2020).  A step-by-step guide to conducting an integrative review . Springer.

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When is an Integrative Review methodology appropriate?

Outline of stages, methods and guidance, examples of integrative reviews, supplementary resources.

"An integrative review is a specific review method that summarizes past empirical or theoretical literature to provide a greater comprehensive understanding of a particular phenomenon or healthcare problem" (Broome, 1993). Thus, integrative reviews have the potential to build upon nursing science, informing research, practice, and policy initiatives.

An integrative review method is an approach that allows for the inclusion of diverse methodologies (i.e. experimental and non-experimental research) and have the potential to play a greater role in evidence-based practice for nursing ( Whittemore & Knafl, 2005 ).

Characteristics:

• An integrative review is best designed for nursing research
• The problem must be clearly defined
• define concepts
• review theories
• review evidence/point out gaps in the literature
• analyze methodological issues

When to Use It: According to Toronto & Remington (2020) , Whittmore & Knafl (2005) , and Broome (2000)  an integrative review approach is best suited for:

• A research scope focused more broadly at a phenomenon of interest rather than a systematic review and allows for diverse research, which may contain theoretical and methodological literature to address the aim of the review.
• Supporting a wide range of inquiry, such as defining concepts, reviewing theories, or analyzing methodological issues.
• Examining the complexity of nursing practice more broadly by using diverse data sources.

The following stages of conducting an integrative review are derived from  Whittemore & Knafl (2005) .

Timeframe:  12+ months

*Varies beyond the type of review. Depends on many factors such as but not limited to: resources available, the quantity and quality of the literature, and the expertise or experience of reviewers" ( Grant & Booth, 2009 ).

Question:  Formulation of a problem, may be related to practice and/or policy especially in nursing.

Is your review question a complex intervention?  Learn more about  Reviews of Complex Interventions .

Sources and searches:  Comprehensive but with a specific focus, integrated methodologies-experimental and non-experimental research. Purposive Sampling may be employed. Database searching is recommended along with grey literature searching. "Other recommended approaches to searching the literature include ancestry searching, journal hand searching, networking, and searching research registries." Search is transparent and reproducible.

Selection:  Selected as related to problem identified or question, Inclusion of empirical and theoretical reports and diverse study methodologies. 

Appraisal:  "How quality is evaluated in an integrative review will vary depending on the sampling frame." Limited/varying methods of critical appraisal and can be complex. "In a review that encompasses theoretical and empirical sources, two quality criteria instruments could be developed for each type of source and scores could be used as criteria for inclusion/exclusion or as a variable in the data analysis stage."

Synthesis:  Narrative synthesis for qualitative and quantitative studies. Data extracted for study characteristics and concept. Synthesis may be in the form of a table, diagram or model to portray results. "Extracted data are compared item by item so that similar data are categorized and grouped together."  

The method consists of:

• data reduction
• data display
• data comparison
• conclusion drawing,
• verification 

The following resources are considered to be the best guidance for conduct in the field of integrative reviews.

Methods & Guidance

• Hopia, H., Latvala, E., & Liimatainen, L. (2016). Reviewing the methodology of an integrative review .  Scandinavian journal of caring sciences ,  30 (4), 662–669. doi: 10.1111/scs.12327
• Russell C. L. (2005). An overview of the integrative research review .  Progress in transplantation ,  15 (1), 8–13. doi: 10.1177/152692480501500102
• Whittemore, R., & Knafl, K. (2005). The integrative review: updated methodology .  Journal of advanced nursing ,  52 (5), 546–553. doi: 10.1111/j.1365-2648.2005.03621.x

Reporting Guideline

There is currently no reporting guideline for integrative reviews.

• Collins, J. W., Zoucha, R., Lockhart, J. S., & Mixer, S. J. (2018). Cultural aspects of end-of-life care planning for African Americans: an integrative review of literature .  Journal of transcultural nursing ,  29 (6), 578–590. doi: 10.1177/1043659617753042
• Cowdell, F., Booth, A., & Appleby, B. (2017). Knowledge mobilization in bridging patient-practitioner-researcher boundaries: a systematic integrative review protocol .  Journal of advanced nursing ,  73 (11), 2757–2764. doi: 10.1111/jan.13378
• Frisch, N. C., & Rabinowitsch, D. (2019). What's in a definition? Holistic nursing, integrative health care, and integrative nursing: report of an integrated literature review .  Journal of holistic nursing ,  37 (3), 260–272. doi: 10.1177/0898010119860685
• Kim, J., Kim, Y. L., Jang, H., Cho, M., Lee, M., Kim, J., & Lee, H. (2020). Living labs for health: an integrative literature review .  European journal of public health ,  30 (1), 55–63. doi: 10.1093/eurpub/ckz105
• Luckett, T., Sellars, M., Tieman, J., Pollock, C. A., Silvester, W., Butow, P. N., Detering, K. M., Brennan, F., & Clayton, J. M. (2014). Advance care planning for adults with CKD: a systematic integrative review .  American journal of kidney diseases ,  63 (5), 761–770. doi: 10.1053/j.ajkd.2013.12.007
• Shinners, L., Aggar, C., Grace, S., & Smith, S. (2020). Exploring healthcare professionals' understanding and experiences of artificial intelligence technology use in the delivery of healthcare: an integrative review .  Health informatics journal ,  26 (2), 1225–1236. doi: 10.1177/1460458219874641
• Silva, D., Tavares, N. V., Alexandre, A. R., Freitas, D. A., Brêda, M. Z., Albuquerque, M. C., & Melo, V. L. (2015). Depressão e risco de suicídio entre profissionais de Enfermagem: revisão integrative [Depression and suicide risk among nursing professionals: an integrative review] .  Revista da Escola de Enfermagem da U S P ,  49 (6), 1027–1036. doi: 10.1590/S0080-623420150000600020
• Stormacq, C., Van den Broucke, S., & Wosinski, J. (2019). Does health literacy mediate the relationship between socioeconomic status and health disparities? integrative review .  Health promotion international ,  34 (5), e1–e17. doi: 10.1093/heapro/day062
• Broome M.E. (1993). Integrative literature reviews for the development of concepts. In Rodgers, B. L., & Knafl, K. A. (Eds.),  Concept development in nursing  (2nd ed., pp. 231-250). W.B. Saunders Company.
• da Silva, R. N., Brandão, M., & Ferreira, M. A. (2020). Integrative Review as a Method to Generate or to Test Nursing Theory .  Nursing science quarterly ,  33 (3), 258–263. doi: 10.1177/0894318420920602
• Garritty, C., Gartlehner, G., Nussbaumer-Streit, B., King, V. J., Hamel, C., Kamel, C., Affengruber, L., & Stevens, A. (2021). Cochrane Rapid Reviews Methods Group offers evidence-informed guidance to conduct rapid reviews .  Journal of clinical epidemiology ,  130 , 13–22. doi: 10.1016/j.jclinepi.2020.10.007
• Grant, M. J., & Booth, A. (2009). A typology of reviews: an analysis of 14 review types and associated methodologies .  Health information and libraries journal ,  26 (2), 91–108. doi: 10.1111/j.1471-1842.2009.00848.x

Toronto, C. E., & Remington, R. (2020).  A Step-By-Step Guide to Conducting an Integrative Review.  Springer International Publishing AG. doi: 10.1007/978-3-030-37504-1

• Torraco, R. J. (2005). Writing integrative literature reviews: guidelines and examples .  Human Resource Development Review, 4 (3), 356–367. doi: 10.1177/1534484305278283
• Whittemore. (2007). Rigour in Integrative Reviews . In Webb, C., & Roe, B. (Eds.),  Reviewing Research Evidence for Nursing Practice (pp. 149–156). Blackwell Publishing Ltd. https://doi.org/10.1002/9780470692127.ch11
• << Previous: Mapping Review
• Next: Rapid Review >>

Other Names for an Integrative Review

• Integrative Literature Review
• Systematic Integrative Review
• Integrative Research Review

Limitations of an Integrative Review

The following challenges of integrative reviews are derived from Toronto & Remington (2020) , Whitmore & Knafl (2005) , and Broome (2000) .

• The combination and complexity of incorporating diverse methodologies can contribute to lack of rigor, inaccuracy, and bias.
• Methods of analysis, synthesis, and conclusion-drawing remain poorly formulated.
• Combining empirical and theoretical reports can be difficult.
• There is no current guidance on reporting.

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An overview of the integrative research review

Affiliation.

• 1 University of Missouri-Columbia, Columbia, MO, USA.
• PMID: 15839365
• DOI: 10.1177/152692480501500102

The integrative literature review has many benefits to the scholarly reviewer, including evaluating the strength of the scientific evidence, identifying gaps in current research, identifying the need for future research, bridging between related areas of work, identifying central issues in an area, generating a research question, identifying a theoretical or conceptual framework, and exploring which research methods have been used successfully. The 5-stage integrative review process includes (1) problem formulation, (2) data collection or literature search, (3) evaluation of data, (4) data analysis, and (5) interpretation and presentation of results. Maintaining scientific integrity while conducting an integrative research review involves careful consideration to threats to validity. Strategies to overcome these threats are reviewed. The integrative review methodology must involve detailed and thoughtful work, the outcome of which can be a significant contribution to a particular body of knowledge and, consequently, to practice and research.

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Advancing offshore renewable energy: integrative approaches in floating offshore wind turbine-oscillating water column systems using artificial intelligence-driven regressive modeling and proportional-integral-derivative control.

1. Introduction

2. technical literature review, 3. theoretical foundations, 3.1. model of wave elevation, 3.2. wind turbine, 3.3. equation of motion of the hybrid fowt-owcs, 4. proposed hybrid fowt-owc model, 4.1. platform geometry design, 4.2. advanced hydrostatic and hydrodynamic computations, 4.3. advanced deep learning-based hybrid modeling for the hybrid system, 4.4. proposed control integration: pid gain scheduling approach, 5. model validation and control results, 6. conclusions, author contributions, data availability statement, conflicts of interest.

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Ahmad, I.; M’zoughi, F.; Aboutalebi, P.; Garrido, A.J.; Garrido, I. Advancing Offshore Renewable Energy: Integrative Approaches in Floating Offshore Wind Turbine-Oscillating Water Column Systems Using Artificial Intelligence-Driven Regressive Modeling and Proportional-Integral-Derivative Control. J. Mar. Sci. Eng. 2024 , 12 , 1292. https://doi.org/10.3390/jmse12081292

Ahmad I, M’zoughi F, Aboutalebi P, Garrido AJ, Garrido I. Advancing Offshore Renewable Energy: Integrative Approaches in Floating Offshore Wind Turbine-Oscillating Water Column Systems Using Artificial Intelligence-Driven Regressive Modeling and Proportional-Integral-Derivative Control. Journal of Marine Science and Engineering . 2024; 12(8):1292. https://doi.org/10.3390/jmse12081292

Ahmad, Irfan, Fares M’zoughi, Payam Aboutalebi, Aitor J. Garrido, and Izaskun Garrido. 2024. "Advancing Offshore Renewable Energy: Integrative Approaches in Floating Offshore Wind Turbine-Oscillating Water Column Systems Using Artificial Intelligence-Driven Regressive Modeling and Proportional-Integral-Derivative Control" Journal of Marine Science and Engineering 12, no. 8: 1292. https://doi.org/10.3390/jmse12081292

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Peer-reviewed

Research Article

Optimizing wave energy converter benchmarking with a fuzzy-based decision-making approach

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation College of Technology and Design, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam

Roles Conceptualization, Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Nhat-Luong Nhieu, 
• Tri Dung Dang

• Published: July 26, 2024
• https://doi.org/10.1371/journal.pone.0307894
• Reader Comments

The quest for sustainable energy solutions has intensified interest in marine renewables, particularly wave energy. This study addresses the crucial need for an objective assessment of Wave Energy Converter (WEC) technologies, which are instrumental in harnessing ocean waves for electricity generation. To benchmark WEC technologies, we employed an integrated approach combining the MEthod based on the Removal Effects of Criteria (MEREC) and the Spherical Fuzzy Combine Compromise Solution (SF-CoCoSo). MEREC provided a systematic way to determine the importance of various benchmarking criteria, while SF-CoCoSo facilitated the synthesis of complex decision-making data into a coherent evaluation score for each technology. The results of the study offer a definitive ranking of WEC technologies, with findings emphasizing the importance of grid connectivity and adaptability to various wave conditions as pivotal to the technologies’ success. While the study makes significant strides in the evaluation of WECs, it also recognizes limitations, including the potential for evolving market dynamics to influence criteria weightings and the assumption that the MCDM methods capture all decision-making complexities. Future work should expand the evaluative criteria and explore additional MCDM methods to validate and refine the benchmarking process further.

Citation: Nhieu N-L, Dang TD (2024) Optimizing wave energy converter benchmarking with a fuzzy-based decision-making approach. PLoS ONE 19(7): e0307894. https://doi.org/10.1371/journal.pone.0307894

Editor: Mehdi Keshavarz-Ghorabaee, Gonbad Kavous University, ISLAMIC REPUBLIC OF IRAN

Received: January 27, 2024; Accepted: July 12, 2024; Published: July 26, 2024

Copyright: © 2024 Nhieu, Dang. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: This research was funded by the University of Economics Ho Chi Minh City, Vietnam. This paper is a product of a university-level research project code CTD-2023-05 funded by the University of Economics Ho Chi Minh City. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The quest for sustainable and renewable energy sources has led to a growing interest in wave energy, recognized for its immense potential and significance [ 1 ]. Unlike other renewable sources, wave energy offers a consistent and powerful supply, largely untapped and capable of meeting global energy demands multiple times over [ 2 ]. Its exploitation promises a revolution in the energy sector, providing a clean, inexhaustible energy source that could significantly reduce our reliance on fossil fuels [ 3 ]. The environmental benefits of wave energy are notable as well, offering a greener alternative that minimizes carbon footprint and ecological disruption, making it a pivotal element in the transition towards sustainable energy solutions [ 4 ]. As wave energy technology evolves, the importance of benchmarking WEC technologies cannot be overstated. Benchmarking serves as a critical evaluative process to compare and contrast different WEC systems, aiming to identify the most effective and cost-efficient among them [ 5 ]. This process is vital for the continual improvement and innovation within the field of wave energy. It provides valuable insights for developers, investors, and policymakers, helping to shape future developments, allocate resources wisely, and establish industry standards [ 6 ]. Effective benchmarking can accelerate the adoption of wave energy by highlighting successful technologies and practices, thereby paving the way for wider acceptance and implementation.

In addressing the intricate task of benchmarking WEC technologies, Multiple Criteria Decision-Making (MCDM) methods emerge as powerful tools. These methods enable a holistic and nuanced analysis by considering a wide range of criteria, from technical performance and economic feasibility to environmental impact and social acceptance [ 7 , 8 ]. MCDM facilitates balanced evaluation, accommodating the multifaceted nature of decision-making in this context. This approach is especially pertinent given the diverse and sometimes conflicting criteria involved in assessing wave energy technologies, ensuring that decisions are well-rounded and robust [ 9 ]. In widely used MCDM methods, the MEthod based on the Removal Effects of Criteria (MEREC) stands out for its objective approach to determining the weight of various criteria. It systematically analyzes the impact of removing a criterion, thereby revealing its relative importance in the overall decision-making process [ 10 ]. This method ensures that each criterion’s contribution is accurately reflected, leading to more balanced and equitable decision-making. On the other hand, the Spherical Fuzzy Combine Compromise Solution (SF-CoCoSo) method introduces an advanced level of decision analysis by incorporating spherical fuzzy sets. Unlike conventional fuzzy numbers, which typically model uncertainty using a single membership function, spherical fuzzy numbers extend this concept by incorporating three-dimensional membership, non-membership, and hesitancy degrees [ 11 ]. This richer representation allows SFNs to capture a more nuanced and accurate portrayal of uncertainty and vagueness inherent in human judgments. Traditional fuzzy sets and their extensions, such as intuitionistic and Pythagorean fuzzy sets, primarily focus on two dimensions, limiting their ability to fully encompass the complexities of decision-making scenarios. In contrast, SFNs offer enhanced flexibility and expressiveness, providing a comprehensive framework that improves the robustness and precision of MCDM analyses [ 12 , 13 ]. This makes spherical fuzzy sets particularly useful in scenarios where decision data are highly uncertain and subject to multiple interpretations, thereby enhancing the overall reliability and effectiveness of the decision-making process [ 14 , 15 ]. This approach allows for a more nuanced representation of uncertainty and vagueness inherent in human judgments [ 16 ]. SF-CoCoSo synthesizes these fuzzy evaluations into a comprehensive compromise solution, skillfully balancing between the best and most feasible options. The integration of MEREC and SF-CoCoSo in benchmarking WEC technologies promises a more refined, accurate, and comprehensive assessment, paving the way for identifying the most promising and efficient wave energy converters. This innovative combination marks a significant advancement in the field, offering robust tools for tackling the complexities of technology assessment in renewable energy systems.

Despite comprehensive insights from the literature on WECs and MCDM, a research gap exists in integrating advanced fuzzy logic with objective weighting methods for WEC benchmarking. Specifically, studies leveraging spherical fuzzy sets with the MEREC method are scarce. This presents an opportunity to improve objectivity and precision in WEC assessments by addressing uncertainty and subjectivity. Furthermore, while methods like CoCoSo balance competing criteria, their application in WEC benchmarking, especially with spherical fuzzy logic, remains underexplored. This study aims to fill these gaps by developing a fuzzy-based, objectively weighted decision-making approach, refining the methodology for sustainable energy decisions.

The motivation behind employing an integrated MCDM approach in this study stems from the recognition of the complex and multi-dimensional challenges inherent in benchmarking WEC technologies. By combining various MCDM methodologies, this approach seeks to address the diverse set of criteria involved in evaluating WEC technologies. This integration aims to refine the decision-making process, enhancing its accuracy, comprehensiveness, and reliability. It represents an innovative step forward in tackling the intricate task of benchmarking in the wave energy sector, potentially leading to more informed and effective decisions.

This study is primarily aimed at advancing the benchmarking process of WEC technologies through the integration of two distinct methodologies: the objective weighting capabilities of the MEREC and the nuanced decision analysis afforded by the SF-CoCoSo method. By fusing these approaches, the research endeavors to provide a comprehensive and balanced evaluation of WEC technologies. The pivotal role of benchmarking WEC technologies for advancing wave energy as a viable and sustainable energy source is underscored. It not only identifies leading technologies but also informs policy, guides research and development efforts, and encourages industry-wide standards and best practices [ 6 ].

An innovative, integrated MCDM approach to the benchmarking of WEC technologies is contributed by this study, promising to enhance the clarity, accuracy, and effectiveness of technology assessments. Through this pioneering methodology, the strategic development and deployment of wave energy converters are aimed to be supported, marking a crucial step forward in the sustainable harnessing of wave energy.

2. Literature review

2.1. wecs technology studies.

The literature on WECs is extensive, reflecting the diversity of designs and approaches to harnessing wave power. Fundamental to the body of research is the principle that WECs must be efficient, durable, and environmentally sustainable to be viable long-term solutions. Early studies trace the historical development of WECs, noting initial concepts dating back to the 1970s. Over the years, various designs have been proposed, such as point absorbers (oscillating bodies), attenuators, and oscillating water columns, each suited to different marine environments and wave conditions. Comparative analyses, such as those by Harris et al. (2004) or Folley and Whittaker (2010), provide a comprehensive overview of these technologies, discussing their operating principles, energy conversion mechanisms, and typical locations [ 17 , 18 ]. A significant portion of the literature focuses on performance metrics for WECs. Researchers have established various efficiency indicators, including capture width ratio and power matrix, as benchmarks. Studies by Aderinto & Li (2019) and Majidi et al. (2021) have been instrumental in defining these metrics, which are crucial for understanding and improving WEC technology [ 19 , 20 ]. The environmental impact of WECs is a critical aspect explored in the literature. Many studies examine the ecological effects of WEC installations, including potential impacts on marine life and habitats [ 21 , 22 ]. Economically, the viability of WECs is often analyzed through cost-benefit analyses, levelized cost of energy (LCOE), and market potential assessments. Notable contributions by Chang et al. evaluate the economic challenges and opportunities for WEC technologies [ 23 ]. Studies concerning the technology readiness level (TRL) of WECs highlight the maturity of different WEC designs and their readiness for commercial deployment. Bertram et al. provide a TRL framework specific to WECs, while Magagna and Uihlein discuss the roadmaps and strategic actions required to advance WEC technologies to higher TRLs in Europe [ 24 , 25 ]. The literature also delves into the challenges faced by WECs, such as those related to maintenance, scalability, and grid integration [ 1 ]. More recent literature reflects the ongoing innovations in WEC technology. Studies on new materials, advanced control systems, and optimization algorithms are frequent, with researchers exploring novel approaches to improve efficiency and resilience [ 2 , 26 ]. The advent of digital twin technology and its application to WECs, as examined by Katsidoniotaki et al., exemplifies the technological advancements in this field [ 27 ]. Benchmarking studies, essential for comparative analysis and policy-making, have become increasingly prevalent. The application of MCDM methods to WEC technology assessment is a relatively recent trend in the literature, addressing the complexity of evaluating multiple performance criteria [ 7 , 24 , 28 ].

The literature on WEC technologies presents a multidimensional view of the field, covering a range of topics from foundational concepts to cutting-edge innovations. The collective research underscores the potential of WECs as a sustainable energy source while acknowledging the technical, environmental, and economic challenges that must be addressed. The continuous evolution of benchmarking methodologies, including MCDM approaches, reflects the dynamic nature of the field and the ongoing effort to optimize WEC technologies for global energy portfolios.

2.2. Studies of MCDM approaches

One of the critical steps in MCDM is the determination of the weights of criteria, which can significantly influence the final decision. The literature distinguishes between two main weight assignment methods: subjective and objective. Subjective methods rely on the judgment and preferences of the decision-maker. Techniques such as the Analytic Hierarchy Process (AHP) and Delphi method are widely discussed in the literature for their ability to capture expert opinions and preferences [ 29 – 31 ]. These methods, however, may introduce bias and are dependent on the expertise and consistency of the judgments provided. In contrast, objective methods determine weights based on the inherent data structure of the decision matrix, without relying on external judgments [ 32 ]. Techniques like Entropy and the CRiteria Importance Through Intercriteria Correlation (CRITIC) methods are frequently cited for their ability to reduce subjectivity by extracting weights from the variation in the criteria data [ 32 – 34 ]. The MEthod based on the MEREC is an objective weighting method that assesses the importance of criteria based on the sensitivity of the decision-making process to the removal of each criterion [ 10 ]. MEREC stands out in the literature for its unique approach to understanding the interdependencies and impact of each criterion on the overall decision-making process.

Compromise solution-based methods aim to find a solution that is the closest to the ideal and furthest from the anti-ideal solution. These methods are based on the concept of satisfying decision-making, where the goal is not to maximize or minimize individual criteria but to find a solution that is acceptable across all criteria [ 35 ]. The literature on compromise solution-based methods is rich with studies on the VIKOR method, which introduces the idea of ranking and selecting solutions based on their proximity to the ideal solution [ 36 ]. These methods are praised for their ability to provide a balance between different criteria, making them suitable for scenarios with competing and non-commensurable criteria. The Combined Compromise Solution (CoCoSo) method is a relatively recent addition to compromise solution-based MCDM methods. It combines the results of three different compromise ranking methods to derive a comprehensive solution [ 37 ]. The CoCoSo has gained attention for its robustness and the ability to produce a more stable and reliable ranking by mitigating the weaknesses of individual compromise methods [ 38 ].

Fuzzy extensions of traditional MCDM methods, like fuzzy AHP and fuzzy TOPSIS, have been extensively studied and applied across various fields. They are known for their ability to model the uncertainty of subjective assessments and to provide a more nuanced approach to decision-making [ 39 ]. Spherical fuzzy sets (SFS), an extension of fuzzy sets, offer a three-dimensional representation of membership, non-membership, and hesitancy degrees, providing an even more refined modeling of uncertainty [ 13 ]. The exploration of SFS in decision-making processes has gained significant momentum in recent research, emphasizing its effectiveness in handling uncertainty and vagueness across various domains. Kaushik D. and Sankar K.R. (2023) ventured into the T-spherical fuzzy set (T-SFS) domain, developing a hybrid form of operators to address biasness and ensure unbiased decision-making in MADM problems [ 40 ]. Their work highlighted the advantages of using weighted power partitioned neutral average and geometric operators within the T-SFS environment, showcasing its application in hydrogen refueling station site selection. Muhammad Saad and Ayesha Rafiq (2023) further expanded the utility of T-SFS by introducing correlation coefficients for T-SFS, demonstrating their application in pattern recognition and decision-making, notably in selecting a suitable COVID-19 mask [ 41 ]. Arun Sarkar et al. (2023) introduced an innovative model, the T-spherical fuzzy hypersoft set (T-SFHSS), enhancing the precision of fuzzy set calculations and proposing novel aggregation operators for T-SFHSS [ 42 ]. Their research underscored the model’s superiority in handling imprecise data, illustrated through an application in natural agribusiness. D. Ajay et al (2023) contributed by defining new exponential and Einstein exponential operational laws for SFS, aiming to refine decision-making processes in evaluating psychotherapy methods [ 43 ]. The integration of spherical fuzzy sets into MCDM methods allows for a comprehensive and sophisticated handling of uncertainty, enhancing the decision-making process’s flexibility and expressiveness [ 12 , 16 , 38 , 44 ].

The literature on MCDM approaches reflects a continued evolution from traditional, more subjective methods to sophisticated, data-driven techniques that seek to reduce bias and better handle uncertainty. Objective methods like MEREC, compromise solution methods like CoCoSo, and the application of fuzzy theory, particularly spherical fuzzy sets, represent significant advancements in the field, offering nuanced and robust frameworks for decision-making in complex, multi-criteria environments.

2.3. MCDM Applications for WECs

The application of MCDM approaches in evaluating WECs represents a critical advancement in optimizing renewable energy technologies. MCDM methods facilitate comprehensive analyses by incorporating various technical, economic, social, and environmental criteria, essential for assessing the viability and sustainability of WEC technologies. Recent literature underscores the increasing reliance on MCDM methodologies for WEC assessments. Sadaf Nasrollahi et al. (2023) employed the fuzzy Delphi method and PROMETHEE to select optimal WEC technologies for the Caspian Sea, indicating the preference for Pelamis based on an extensive set of criteria [ 7 ]. Shadmani et al. (2023) developed a novel MCDM strategy integrating exploitable wave energy storage and production metrics to select optimal sites for WEC deployment along Oman’s coast [ 28 ]. Meng Shao et al. (2024) combined GIS, MCDM, and ANN techniques to enhance site selection and wave power forecasting for WPPs, focusing on Hainan Island and identifying suitable areas for deployment [ 45 ]. Daekook Kang et al. (2024) introduced an innovative hybrid MCDM methodology using fuzzy SWARA and ELECTRE for selecting the most suitable WEC, highlighting the point absorber technology [ 46 ]. Shabnam et al. (2023) emphasized the importance of combining offshore wind and wave energy through MCDM methods to identify the best locations for constructing combined farms, yet noted the lack of evaluation on seabed conditions and climate change impacts [ 47 ].

The findings from these studies reflect the effectiveness of MCDM approaches in navigating the complexities associated with WEC technology assessment and site selection. These methodologies offer a structured framework for integrating diverse criteria, ensuring a holistic evaluation of potential technologies and locations. The adoption of fuzzy logic and other advanced techniques further enhances the decision-making process, accommodating uncertainty and subjectivity in assessments. Despite the progress in applying MCDM to WEC assessments, a notable gap remains in fully exploiting the potential of fuzzy-based objective weighting methods, particularly in integrating spherical fuzzy sets with the MEREC method. Most studies focus on traditional MCDM approaches without fully embracing the advancements in fuzzy logic to handle uncertainty and hesitancy more effectively.

While the existing literature on WECs and Multi-Criteria Decision-Making MCDM approaches offers comprehensive insights into the technical, environmental, and economic aspects of WEC technologies, as well as various methodologies for their evaluation, a noticeable research gap persists in the integration of advanced fuzzy logic with objective weighting methods for the benchmarking of WEC technologies. Specifically, there is a scarcity of studies that leverage the nuanced capabilities of spherical fuzzy sets in conjunction with the MEREC method to refine the weighting and evaluation process. This gap indicates an opportunity for a novel approach that enhances the objectivity and precision of WEC technology assessment by accounting for the inherent uncertainty and subjectivity in decision-making processes. Additionally, while compromise solution-based methods like CoCoSo have been recognized for their robustness in balancing competing criteria, their application in the context of WEC technology benchmarking, particularly when integrated with spherical fuzzy logic, remains underexplored. This study aims to bridge these gaps by developing and applying a fuzzy-based, objectively weighted integrated decision-making approach, thereby contributing a refined methodology for benchmarking WEC technologies that better reflects the complex, multidimensional nature of sustainable energy decisions.

3. Methodology

3.1. preliminary.

Fuzzy theory, introduced by Lotfi Zadeh in the 1960s, is a mathematical framework for dealing with uncertainty and imprecision, challenging the traditional binary logic of true or false by introducing degrees of truth [ 48 ]. This theory allows for more nuanced and realistic modeling of complex systems where clear-cut boundaries do not exist [ 49 ]. Building on this concept, SFS, a more recent development in fuzzy logic, offers an advanced approach to handling uncertainty as shown in Fig 1 [ 13 ]. It extends the traditional fuzzy set and intuitionistic fuzzy set by incorporating a third parameter, which enhances its ability to represent and process vagueness and ambiguity in data [ 50 ]. This three-dimensional representation in spherical fuzzy sets offers a more comprehensive and flexible tool for dealing with uncertain information, making it valuable in fields like artificial intelligence, decision-making, and complex system analysis [ 51 ].

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https://doi.org/10.1371/journal.pone.0307894.g001

The parameters ϑ Ñ ( s ), μ Ñ ( s ), and π Ñ ( s ) are the membership degree, non-membership degree, and hesitancy degree of each s to Ñ , respectively.

3.2. The proposed spherical fuzzy objectively weighting integrated decision-making approach

To take advantage of the advantages of MEREC and SF-CoCoSo, this study introduces an integrated approach which is performed according to the following procedure:

As described in Table 1 , SFN Ẽ k representing the expertise of experts provided by analysts or higher-level decision-makers in linguistic terms based on expert attributes such as years of experience, qualifications.

https://doi.org/10.1371/journal.pone.0307894.t001

Step 2: The benchmarking criteria ( j = 1 … J ), and alternatives ( i = 1 … I ) are defined based on literature review and experts’ opinions.

Step 3. Experts provide linguistic assessments of alternatives according to the criteria. These linguistic assessments are then transformed into the corresponding SFNs as shown in Table 2 , which is provided by experts or decision makers, to form SF decision matrices. SF decision matrices are represented as Eq (17) . In other applications, the linguistic scale can be defined by decision makers or experts. The SFN values should be symmetrically distributed around the neutral point, designated as "Medium" (0.500, 0.500, 0.500), ensuring a balanced and consistent progression in the evaluation scale. This symmetry implies that as judgments move from neutral to extremely positive or negative, the membership and non-membership degrees adjust inversely, maintaining logical coherence [ 11 , 15 , 16 , 31 ].

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Step 5. The crisp decision matrix is constructed according to Eq (15) .

Step 7. The overall performance of the alternatives ( S i ) is calculated using a logarithmic measure with equal criteria weights according to Eq (23) . It is based on the non-linear function as shown in Fig 2 . Therefore, the smaller value of m ij yield higher value of S i .

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Step 9. The weights of criteria ( w j ) are computed based on the removal effect ( E j ) of the jth criterion according to Eq (25) .

Step 13. The final evaluation score (Φ i ) of alternatives is determined as Eq (31) . The final rank of alternatives is ranked in descending order of the value of Φ i . In other words, the best alternative has the largest value of Φ i .

4. Numerical results

4.1. wecs benchmarking by the proposed approach.

Based on the references, the WEC technologies that can be considered during benchmarking include the Oscillating water columns (OWC), Point absorbers (PAB), Attenuators (ATE), and Overtopping devices (OTD). Moreover, we add two more Ocean Energy Systems which are Salinity gradient power (SGP) and Tidal stream turbines (TST) to the benchmarking to be evaluated [ 44 ]. Oscillating water columns (OWCs) utilize the movement of water in a chamber to drive an air turbine for electricity generation, making them a straightforward and efficient choice, primarily suited for regions with robust wave action. Point absorbers or oscillating bodies, anchored to the seabed, translate their motion with the waves into electricity through hydraulic or mechanical systems, offering versatility across different wave conditions. Attenuators, designed to absorb wave energy and dissipate it as heat or sound, serve the dual purpose of coastline protection and electricity generation. Salinity gradient power (SGP) devices, though still in early development stages, exploit salinity differences between seawater and freshwater as a potential cost-effective source of wave energy. Tidal stream turbines, resembling wind turbines but designed for tidal currents, are adaptable for both shallow and deep waters, representing a mature technology. Lastly, overtopping devices collect water from wave overtopping barriers to drive turbines or pumps efficiently, suitable for regions with high wave heights.

To perform benchmarking of WEC technologies, a group of six experts was first convened to conduct a survey using the Delphi method. Based on their expertise, as shown in Table 3 , the corresponding SFN is recommended according to Table 1 . After that, the experts’ weights are calculated according to Eq (16) and shown in Table 3 . In the next step, through the Delphi method interview process, experts propose benchmarking criteria (BC) as well as provide linguistics judgments for WECs technologies corresponding to each BC. Table 4 below presents the BAs and linguistics judgments of the first expert. Based on the corresponding SFNs in Table 2 , each expert’s linguistic judgments were converted into SFNs. The result of this process is the formation of individual SF benchmarking matrices. Based on the weights of the experts, which were obtained above, the individual benchmarking matrices are aggregated according to Eqs (18) and (19) . The aggregated SF benchmarking matrix is shown in Table 5 . To start the procedure to determine the objective weights of the BCs, the defuzzification and normalization process is performed according to Eqs (15) and (22) , respectively. As the results, the obtained the crisp benchmarking matrix and the normalized benchmarking matrix are presented in Table A1 and Table A2 in S1 Appendix . As described in Eqs (23) – (25) , the removal effects of the benchmarking criteria are calculated as shown in Table 6 .

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Then, the objective weight of the benchmarking criteria is determined according to Eq (26) and are illustrated in Fig 3 . In the lights of weighting results, the highest weight assigned to Grid Connection, at 0.18, underscores the paramount importance of the ability of WECs to integrate with existing power grid infrastructures, a critical aspect for the practical deployment of these technologies. The Required Wave Conditions follow closely, with a weight of 0.15, reflecting the necessity for WEC technologies to operate efficiently across diverse marine environments, ensuring reliability and consistency in energy production. Cost, with a weight of 0.11, is emphasized as a major consideration, indicating that the economic viability of WEC technologies is crucial for their market penetration and scalability. Efficiency and Ease of Installation and Maintenance both garner a significant weight of 0.09, highlighting the balance between the effectiveness of energy conversion and the practical aspects of technology deployment and upkeep, which are key to the sustainable adoption and operation of WECs. Moderately weighted factors include Robustness and Environmental Impact, both at 0.07, suggesting these aspects are important but may not be as critical in differentiating between technologies as the top-weighted criteria. Survivability, at 0.06, also receives a moderate emphasis, indicating that the resilience of WECs to extreme marine conditions is an important factor in their overall evaluation. Power Output and Operating Range, each with a weight of 0.03, suggest that while these factors are integral to the function of WECs, they may be considered baseline expectations and not the primary drivers of technology selection. The Technology Readiness Level, at 0.08, is given a mid-range weight, which signifies a balanced emphasis on the maturity and development stage of the technology in the decision-making process. On the other hand, Social Acceptance is assigned the least weight, at 0.02, indicating that while societal factors are acknowledged, they may not be as influential in the technical and economic assessment phases of WEC technologies.

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4.2. Sensitivity analysis

In this section, the sensitivity analysis of the stability and flexibility coefficient ( δ ) with respect to the benchmarking results of WEC technologies reveals insightful trends and implications. The analysis spans a range of δ from 0.1 to 0.9, illustrating how variations in this coefficient impact the ranking and performance evaluation of WEC technologies as shown in Figs 4 and 5 .

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https://doi.org/10.1371/journal.pone.0307894.g005

The OWC technology shows remarkable stability in its performance with scores consistently at 1.8 across a wide range of δ values, only increasing slightly to 1.9 at δ = 0.9. This consistency suggests that OWC technology’s performance is less sensitive to changes in the stability and flexibility coefficient, indicating a robust and reliable technology option. PAB and SGP technologies exhibit a high degree of sensitivity to changes in δ , with their scores increasing significantly as the coefficient rises. Specifically, PAB scores increase from 2.1 to 5.0, and SGP scores from 1.9 to 5.4 as δ increases from 0.1 to 0.9. This indicates that these technologies’ perceived effectiveness and suitability for wave energy conversion can vary greatly depending on the stability and flexibility requirements of the evaluation criteria. ATE shows a decreasing trend in performance as the δ value increases, with scores decreasing from 1.8 to 1.4. This trend suggests that ATE technology may become less favorable as more emphasis is placed on stability and flexibility in the decision-making process.

Notably, at higher δ values, PAB, SGP, TST, and OTD show marked increases in their scores, indicating a stronger preference for these technologies under conditions that highly value stability and flexibility. This shift emphasizes the importance of considering the operational environment and specific project needs when selecting WEC technologies. The dramatic increases in scores for PAB, SGP, TST, and OTD at high δ values (particularly at 0.9) suggest that the SF-CoCoSo method, under high stability and flexibility coefficients, may overestimate the advantages of certain technologies. This potential for overestimation underscores the necessity for careful consideration and calibration of the δ value to reflect realistic operational expectations and requirements.

5. Discussion

The benchmarking results for WEC technologies, as illustrated in Fig 6 , offer a detailed perspective on the performance and suitability of each technology in relation to the criteria deemed important for successful deployment in the wave energy sector. At the forefront of these results is the PAB technology, which has achieved the highest score of 2.0286. This superior score suggests that PAB technology likely excels in several key areas such as efficiency, cost, grid compatibility, and possibly in its ability to operate across a range of wave conditions. The high score may also indicate that PAB technology aligns well with the current priorities and requirements of the industry, including aspects of environmental impact and social acceptance. The leading score of PAB technology points towards its potential as a frontrunner in the wave energy sector, setting a benchmark for others to aspire to. Following PAB, the OWC technology, with a score of 1.8745, and the TST technology, with a score of 1.7432, both show strong performances. These scores suggest that these technologies are likely to be competitive in the market, with strengths that may include robust design, high power output, and operational reliability. The slightly lower scores compared to PAB could indicate areas for improvement or could reflect strategic trade-offs in their design or operation that affect their overall benchmarking score.

https://doi.org/10.1371/journal.pone.0307894.g006

The ATE and SGP technologies are closely ranked, with scores of 1.6079 and 1.6061, respectively. These similar scores may imply that both technologies share comparable capabilities or face similar challenges in meeting the benchmarking criteria. Their position towards the lower end of the performance spectrum could be attributed to factors such as higher costs, lower technology readiness levels, or perhaps less favorable environmental impacts. Nonetheless, these technologies might still offer specific advantages under certain conditions or for applications within the wave energy field. Lastly, the OTD technology, with a middle-lower score of 1.6580, suggests that while it may not excel across all criteria, it possesses a balanced suite of attributes that afford it a respectable place in the benchmarking evaluation. This score might be indicative of a technology with potential, one that could benefit from targeted improvements or could be well-suited to niche applications where its specific strengths are most valuable.

6. Managerial implications

This benchmarking study on WEC technologies using the MEREC and SF-CoCoSo methods offers valuable managerial insights that can significantly influence decision-making in the wave energy sector. For industry leaders and investors, the ranking of WEC technologies provides a strategic guide for directing investments toward the most efficient and reliable options. It allows for an informed allocation of research and development resources, particularly into high-potential areas that could enhance the performance and marketability of WECs. For policymakers, the findings can inform the creation of regulations and incentives that support the advancement and adoption of superior technologies. Manufacturers and developers of WEC technologies can utilize these insights to position their offerings more competitively, emphasizing the strengths identified through the benchmarking process in their marketing and communication strategies. Supply chain decisions can also be optimized based on the study’s outcomes. By aligning supply chain strategies with the production needs of the most promising WEC technologies, companies can achieve greater efficiency and cost-effectiveness. Furthermore, understanding the diverse risk profiles of each technology allows for the development of nuanced risk mitigation strategies, tailored to address specific technological vulnerabilities. The implications extend to investment diversification, suggesting that a balanced portfolio of WEC technologies might spread risk and increase resilience. This is particularly pertinent for companies looking to enter new markets or adapt existing technologies to meet local conditions and regulations. Sustainability considerations are also paramount. Managers can leverage the environmental impact data to steer their companies towards more sustainable practices and technologies, fulfilling corporate social responsibility objectives and enhancing the company’s reputation for environmental stewardship. Lastly, the social acceptance findings, albeit less weighted, are crucial for public relations and stakeholder engagement. They offer a framework for addressing public and governmental concerns, which is essential for obtaining project approvals and fostering community support. In essence, the managerial implications of this study are extensive, impacting investment, strategic planning, and operations. They provide a roadmap for enhancing competitive advantage and contribute to the sector’s progress towards sustainable and socially responsible energy solutions.

7. Conclusions

The study commenced with a focus on the burgeoning field of wave energy, recognizing the substantial untapped potential of ocean waves as a renewable energy source. Given the centrality of WECs in transforming wave power into electricity, the study aimed to evaluate and benchmark WEC technologies to determine the most efficient and viable solutions. To achieve this objective, the study employed an integrated approach, combining the MEREC and the SF-CoCoSo methods. MEREC was utilized to objectively weigh the various criteria crucial for evaluating WEC technologies, while SF-CoCoSo aided in aggregating and analyzing the complex decision-making data to derive a final evaluation score for each technology.

The study’s contributions are manifold. It provides a nuanced framework for benchmarking WEC technologies, thereby assisting stakeholders in making informed decisions. Additionally, the study advances the application of integrated MCDM approaches within the renewable energy sector, demonstrating the effectiveness of combining MEREC and SF-CoCoSo in a complex decision-making landscape. Our findings present a clear hierarchy of WEC technologies based on their performance across multiple criteria, including efficiency, cost, environmental impact, and grid connectivity. The study highlights the PAB technology as the front runner, with its superior overall performance, followed by the OWC and TST technologies as strong alternatives. Notably, it also emphasizes the importance of grid connection and adaptability to different wave conditions as critical factors in the benchmarking process.

Despite the valuable insights provided by this study, it recognizes a number of limitations that highlight areas for future exploration and development. The selected criteria for evaluating WEC technologies, while comprehensive, may not fully capture all the dimensions that influence their performance. This limitation opens up an avenue for future research to broaden the scope of evaluative criteria, incorporating emerging factors that could affect WEC technologies as advancements continue and new challenges arise in the field of renewable energy. Furthermore, the objectivity of the criteria weightings, despite being a strength of the current approach, might be subject to the shifting landscapes of the wave energy market and technological evolution. This suggests a need for adaptive methodologies that can dynamically adjust to the changing priorities and innovations within the sector. Future studies could focus on developing more flexible weighting mechanisms that respond to real-time market and technological data, thereby enhancing the relevance and timeliness of the benchmarking process. The assumption that the chosen MCDM methods adequately encapsulate the complexity inherent in the decision-making process for WEC technology assessment may not universally hold true. This indicates a promising research direction in exploring alternative MCDM methods that might offer different perspectives or handle specific aspects of the decision-making process more effectively. The exploration of these alternative methods could reveal new insights and possibly more efficient approaches to benchmarking WEC technologies. In the spirit of continuous improvement, this study serves as a pivotal step towards the systematic and rigorous benchmarking of WEC technologies. It emphasizes the importance of ongoing refinement of the assessment methodologies to align with technological advancements and market developments. Future research is thus encouraged not only to expand the criteria and explore alternative MCDM methods but also to implement strategies for validating the benchmarking process against real-world performance data. Such validation is crucial for ensuring the robustness and relevance of the findings, providing stakeholders with reliable and actionable insights. Moreover, there is an opportunity to integrate advancements in data analytics and artificial intelligence to enhance the benchmarking process. Future work could investigate the application of machine learning algorithms for predictive analysis and trend forecasting in the wave energy domain, offering a forward-looking component to the benchmarking process.

Supporting information

S1 appendix..

https://doi.org/10.1371/journal.pone.0307894.s001

S1 Dataset.

https://doi.org/10.1371/journal.pone.0307894.s002

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Management Decision

ISSN : 0025-1747

Article publication date: 8 July 2024

This article aims to assess risks related to the supply chain 5.0 digitalization. It aims to analyze interdependencies and causal relationships between critical digital supply chain 5.0 risks, emphasizing the need for proactive management to address emerging challenges.

Design/methodology/approach

Through an extensive literature review and expert judgment, risks related to supply chain 5.0 digitalization are identified. An integrated approach for risk assessment is employed, where the Analytic Hierarchy Process (AHP) is utilized to prioritize these risks. Subsequently, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to investigate cause-and-effect relationships among the identified top 10 risks. This comprehensive analysis forms the basis for informed strategic management decision-making.

The analysis identifies significant influences of “Dependence on technology,” “Complexity”, “Potential system failures”, and “Cyber security” while “Environmental impact” and “Socio-economic disparities” emerge as prominent risks in supply chain 5.0 digitalization. These findings offer actionable insights for management decision-making, guiding the formulation of strategies to address and mitigate critical risks.

Practical implications

The proposed integrated approach (AHP-DEMATEL) provides valuable insights for managers to effectively mitigate digital supply chain 5.0 risks and strategically respond to disruptions. By prioritizing risks, organizations can allocate resources efficiently and address the most critical challenges first, minimizing long-term damage to resilience. Embracing this approach enables practitioners to enhance overall supply chain resilience, guiding key management decisions for the development of sustainable and adaptive strategies.

Originality/value

This paper marks the first comprehensive attempt to assess supply chain 5.0 digitalization risks using decision-making methods like AHP and DEMATEL. The integrated approach contributes novel insights to the field of supply chain risk management, specifically aiding management decision-making in the face of digitalization challenges.

• Supply chain management 5.0
• Risk management
• Digitalization

Zekhnini, K. , Chaouni Benabdellah, A. , Bag, S. and Gupta, S. (2024), "Supply chain 5.0 digitalization: an integrated approach for risk assessment", Management Decision , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MD-12-2023-2329

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Strategic assessment of groundwater potential zones: a hybrid geospatial approach

• Original Article
• Open access
• Published: 31 July 2024
• Volume 14 , article number  185 , ( 2024 )

Cite this article

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• Hamid Nazaripour 1 ,
• Mahdi Sedaghat 2 ,
• Vahid Shafaie 3 &
• Majid Movahedi Rad   ORCID: orcid.org/0000-0002-8393-724X 3  

Groundwater aquifers constitute the primary water supply for populations in arid regions, exemplified by the Goharkooh Plain in Iran's driest drainage basin, where conditions of high evapotranspiration and low precipitation prevail. With the escalating demand for water resources, driven mainly by agricultural expansion, the strategic management of groundwater assets has become increasingly critical. This study focuses on delineating groundwater potential zones (GWPZs) through an integrated approach combining multi-criteria decision analysis and geospatial tools. Based on an extensive literature review, nine thematic layers were selected and developed: lithology, geology, drainage density, slope gradient, elevation, vegetation cover, lineament density, land use, and precipitation. These criteria were initially weighted using the analytical hierarchical process (AHP) and subsequently integrated via weighted overlay analysis. In this research, the strategic selection of thematic layers for assessing groundwater potential in arid regions has been identified as an innovative approach that could significantly advance studies in similar settings. The analysis revealed that approximately 60% of the study area, primarily in the southwestern parts, exhibited moderate to very high groundwater potential. This potential is primarily attributed to the presence of alluvial deposits, low drainage density, and favorable slope and elevation conditions. Applying the receiver operating characteristic (ROC) curve yields an area under the curve (AUC) of 81.5%, indicating a relatively high level of predictive accuracy. These findings demonstrate the efficacy of this integrated approach, suggesting its broader applicability in regions with analogous groundwater challenges and management needs.

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Introduction

Water, as a fundamental natural resource, is pivotal in sustaining human life, driving socioeconomic development, and preserving ecological systems (Makonyo and Msabi 2021 ). Presently, water resources worldwide are experiencing substantial stress, attributable to the combined impacts of climatic variability and anthropogenic activities. Factors such as burgeoning population growth, accelerated urbanization, industrial expansion, and intensified agricultural practices have significantly elevated the demand for water (Ghosh et al. 2022 ). This has led to approximately 57% of the water required for domestic, agricultural, and industrial purposes in arid and semiarid regions, such as Iran, being sourced from groundwater reserves. In certain areas, the dependency on groundwater for drinking water escalates to as high as 65%, presenting considerable challenges in its sustainable utilization. Concurrently, the global status of groundwater resources is witnessing a decline, in terms of both quantity and quality, primarily due to excessive extraction. This over-extraction is leading to lower water table levels, and the insufficiency of surface water in these regions is further exacerbating water scarcity issues (Arulbalaji et al. 2019 ). The escalation of groundwater exploitation in arid regions is projected to lead to the utilization of fossil water reserves in the future, a practice that is inherently unsustainable (Scanlon et al. 2006 ).

In light of the scarcity of surface water resources in many regions of Iran, particularly in arid and semiarid areas, groundwater emerges as the most viable resource for fulfilling water requirements (Madani 2014 ; Haji Mohammadi et al. 2024 ). Groundwater is highly prized in arid and semiarid regions not only for its relative freshness and more stable chemical composition, but also for its lower susceptibility to contamination and higher reliability as a water source. Concurrently, it exerts a substantial influence on the ecological capacity of the land, playing an integral role in economic development, ecological diversity, and the health of communities (Izady et al. 2012 ). The pronounced scarcity and depletion of groundwater in the plains of Iran have escalated into a critical and acute crisis (Bagheri and Hosseini 2011 ). Aquifers are encountering formidable challenges in the realm of sustainable development, primarily due to declining water levels, deterioration in water quality, increased vulnerability to climate change as a consequence of global warming, alterations in precipitation patterns, and the frequent occurrence of droughts (Foltz 2002 ; Haji Mohammadi et al. 2024 ).

The Goharkooh Plain in southeastern Iran represents a region with significant agricultural and tourism development potential within an arid landscape. Here, groundwater serves as the sole source for domestic, agricultural, and industrial needs. The region is currently grappling with an intensified water crisis spurred by a steady rise in population and agricultural expansion, which has drastically heightened water demand (Rezaei and Sargezi 2010 ). Addressing this crisis necessitates a concentrated effort to effectively manage water consumption, with a particular focus on identifying and managing the aquifer's water potential.

A critical aspect of this endeavor involves the selection of thematic layers, a process fundamental to delineating groundwater resources yet fraught with challenges, particularly in terms of quantity and type. The suitability of these layers is profoundly influenced by the climatic conditions of the study area, necessitating a nuanced approach, in such a way that the thematic layers effective in delineating groundwater resources in an arid environment with scant rainfall and sparse vegetation cover (Elewa and Qaddah 2011 ; Mallick et al. 2019 ; Mumtaz et al. 2019 ) are completely different from those in a temperate mountainous environment with abundant snowfall and dense forest cover (Sapkota et al. 2021 ). Consequently, the reliability of groundwater potential research hinges on the precise and intelligent selection of factors affecting the groundwater resources of each region. On the other hand, the involvement of numerous thematic layers (Ozdemir 2011 ) as opposed to effective layers (Khan et al. 2022 ) can complicate the analysis process and the interpretation of results. In the context of the Goharkooh Plain's dependence on groundwater, it is critical to examine the factors influencing its availability and dynamics. Groundwater potential is shaped by a variety of factors, including physiography, geology, hydrology, land use, land cover, and climatic elements like precipitation, temperature, and evaporation (Asgher et al. 2022 ; Ifediegwu 2022 ). The complexity and variability of these factors can lead to significant changes in groundwater potential within short distances (Dar et al. 2010 ; Thapa et al. 2017 ). Despite the region's rich groundwater resources, there is a notable lack of comprehensive studies on its potential zones and the factors that influence them. This gap in knowledge has led to unstructured and excessive exploitation of the groundwater resources, resulting in adverse effects such as land subsidence, changes in land use, and restrictions on water usage. Our research aims to address this gap by employing a meticulous approach to selecting thematic layers, thereby enhancing the reliability of groundwater potential assessments in arid environments such as the Goharkooh Plain.

Systematic data integration in hydrogeological research facilitates the rapid and cost-effective identification of potential groundwater areas, offering a significant advancement over traditional, more time-consuming, and expensive methods such as geophysical and hydrogeological surveys, outcrop mapping, and well drilling (Jha et al. 2010 ; Barik et al. 2017 ). Building upon this advancement, remote sensing (RS) technologies and geographical information systems (GIS) emerge as powerful tools for assessing natural resources. They provide high efficiency, low cost, and the capability for complex spatial and spatiotemporal data analyses across extensive areas in a relatively short time frame, presenting a stark contrast to the more traditional methods (Souissi et al. 2018 ). Over the past decade, researchers have employed various variables and numerous statistical techniques with differing accuracies to delineate the boundaries of groundwater potential. These methods encompass a range of approaches, including the weight of evidence model (Tahmassebipoor et al. 2016 ), the probabilistic frequency ratio model (Manap et al. 2014 ; Davoodi Moghaddam et al. 2015 ), the certainty factor (Nampak et al. 2014 ), gamma fuzzy (Antonakos et al. 2014 ), Shannon entropy (Naghibi et al. 2015 ), and multi-criteria decision analysis (Singh et al. 2018 ; Sandoval and Tiburan Jr. 2019 ; Brito et al. 2020 ). Furthermore, machine learning techniques such as random forest (Golkarian et al. 2018 ; Prasad et al. 2020 ), artificial neural networks (Lee et al. 2012 ; Li et al. 2019 ), logistic regression methods (Chen et al. 2018 ), and machine learning ensembles (Kamali Maskooni et al. 2020 ) have also been notable. Among these diverse techniques, RS and GIS have particularly stood out. Compared to conventional hydrogeological survey methods, they have proved effective in producing quick and cost-effective results (Oh et al. 2011 ), especially in arid and water-scarce regions with limited data availability. This field has garnered significant interest in recent decades, with numerous researchers utilizing RS and GIS for groundwater resource exploration (Deepa et al. 2016 ; Chen et al. 2018 ; Rahman et al. 2022 ). The absence of such studies in arid and semiarid regions, particularly in the study area, compelled researchers to employ an integrated approach to delineate zones susceptible to groundwater resources.

This study introduces a groundbreaking methodology in the arid environment of the Goharkooh Plain, where RS technologies, GIS, and multi-criteria decision-making (MCDM) are harmoniously integrated. This methodological innovation establishes a thorough and efficient framework for assessing groundwater potential, with a strong emphasis on the comprehensive evaluation of topological, geological, hydroclimatological, and land cover factors. The primary objective is to effectively utilize this integrated approach to identify and evaluate groundwater potential zones (GWPZs) within the aquifer. By harnessing the collective capabilities of RS, GIS, and MCDM, this research endeavors to overcome the limitations inherent in conventional hydrogeological methods. It is anticipated that this novel approach will significantly bolster sustainable management of aquifer resources, increase per capita income, and enhance groundwater governance in the Goharkooh Plain.

The paper is organized as follows: Sect. " Materials and methods " details the methodology employed in this research, which includes utilizing a four-step method to assess the GWPZs in the Goharkooh Plain. This section covers data collection, developing thematic layers using RS and GIS processing, overlay analysis utilizing the analytical hierarchical process (AHP), and validating and confirming the predicted groundwater potential map. Sect. " Results and discussion " presents the outcomes of our study, encompassing the spatial distribution of thematic layers, the analysis of GWPZs, and the comprehensive validation strategy. Finally, Sect. " Conclusion " provides a concise summary of our findings and their implications.

Materials and methods

The eastern and central regions of Iran, receiving less than 100 mm of rainfall annually, are characterized as hyper-arid and arid climates (Kaboli et al. 2021 ). Owing to the scarcity of annual rainfall, its considerable temporal and spatial variability, and elevated evaporation rates, substantial and consistent surface flows are rarely formed in these regions. The existing surface flows that do occur are typically seasonal and unpredictable, rendering them unreliable for sustained exploitation. Consequently, there is an increased reliance on the extraction of groundwater resources. However, it is essential to note that groundwater reserves in these areas are often not abundant, further complicating their utilization (Safdari et al. 2022 ). The Goharkooh Plain catchment, located in the southeasternmost part of Iran's Lut Desert, spans from 60 degrees 11 min to 61 degrees 7 min East longitude and from 28 degrees 7 min to 28 degrees 45 min North latitude, as depicted in Fig.  1 . This region is characterized by extensive alluvial deposits, which serve as the principal reservoir for groundwater storage, forming the Goharkooh aquifer. The mean elevation of the Goharkooh Plain is estimated to be around 1,350 m above sea level. The total area of the catchment is approximately 2,870 square kilometers, of which about 55 percent consists of low-lying alluvial plains, while the remaining 45 percent is composed of mountainous and highland regions. The Goharkooh aquifer, categorized as a free aquifer, is situated within these alluvial and fan deposits and encompasses an area of about 487 square kilometers (Rezaei and Sargezi 2010 ). In recent decades, the Goharkooh Plain has experienced a significant decline in groundwater levels, primarily attributed to excessive extraction of groundwater resources. This depletion is further compounded by the expansion of agro-industrial complexes and a series of prolonged droughts.

Drainage and location map of the Goharkooh Plain catchment

Methodology

This study employed a systematic four-step method to assess GWPZs in the Goharkooh Plain. This methodology encompassed: (1) comprehensive data collection, (2) the development of thematic layers through RS and GIS processing, (3) the application of overlay analysis using the AHP for identifying GWPZs, and (4) validation and confirmation of the projected GWPZs map using existing well data from the area. Thematic layers were prepared using RS satellite image data and digitization of existing maps using GIS. Nine critical criteria were considered for evaluating groundwater potential in the study area: lithology, geology, drainage density, slope gradient, digital elevation model (DEM), vegetation cover index, lineament density, land use/land cover (LULC), and annual rainfall. These criteria underwent initial assessment through the AHP method, followed by weight assignment, normalization, and ranking, culminating in a model constructed using a weighted overlay index. Additionally, quantitative validation of the predicted groundwater potential map was conducted employing the receiver operating characteristic (ROC) curve analysis, as illustrated in Fig.  2 .

Methodology flowchart of GWPZs

Step 1: data collection

A thorough literature review was conducted to analyze research on the assessment of groundwater potential utilizing GIS and RS methodologies. This review encompassed studies from various regions, including Bangladesh (Adham et al. 2010 ), Egypt (Abdalla 2012 ), India (Gupta and Srivastava 2010 ; Magesh et al. 2012 ; Mukherjee et al. 2012 ; Nag and Ghosh 2013 ; Selvam et al. 2016 ; Deepa et al. 2016 ; Bhave et al. 2019 ), Iran (Rahmati et al. 2015 ), South Korea (Lee et al. 2012 ), Taiwan (Yeh et al. 2016 ), Thailand (Kaewdum and Chotpantarat 2021 ), and Sri Lanka (Senanayake et al. 2016 ). This is aimed at determining the factors that influence groundwater potential. A word cloud visualization (Fig.  3 ) highlights the significance and frequency of each factor in the modeling of GWPZs based on prior studies. This visual format emphasizes key factors by presenting them in progressively larger font sizes to denote their relative importance. A review of research in the field of GWPZs over the past decade reveals that 18 parameters have been predominantly utilized. The nine most significant parameters—slope, drainage density (DD), LULC, lineament density (LD), soil texture (ST), rainfall, geology, geomorphology, and DEM—have featured in approximately 70% of these studies. Intermediate parameters such as the topographic wetness index (TWI), water table depth (WTD), and lithology have each been employed in 17% of the cases. Additionally, indicators including surface water bodies (SWB), vegetation cover (VC), and surface curvature (SC) have been incorporated in 10% of the studies. More novel parameters, including the topographic position index (TPI), utilized in two instances, and the stream power index (SPI) and hydraulic conductivity (HC), each with a usage rate of 3%, have seen minimal application. These factors, intrinsically tied to the characteristics of aquifers, have been explored and validated in regions bearing similarities to the study area. This methodology is anticipated to enhance the reliability of the GWPZs map.

Word cloud of factors for GWPZs used in the past decade studies

Consequently, the data compiled for this study encompass a range of biophysical aspects such as slope, drainage density, LULC, lineament density, rainfall, geology, topography, lithology, and vegetation cover. Additionally, data regarding the location and water level of existing wells in the Goharkooh Plain were also incorporated, with these various datasets being processed to generate thematic layers using diverse geospatial techniques.

Step 2: RS and GIS processing for preparing thematic layers

Gis processing.

Geology and Lithology Geology determines groundwater aquifers. Additionally, the type of rock exposed on the earth's surface, its outcrops, and fractures are directly related to groundwater recharge and feeding (Shaban et al. 2006 ), resulting in different water bodies in each rock. Groundwater is significantly influenced by various types of rock exposed on the earth's surface (O’Leary et al. 2012 ). In studies concerning the potential of water resources, geology investigates the historical alterations in land and soil units over extensive periods. Conversely, lithology focuses on the current physical properties of rock outcrops. Some studies overlook this factor and instead use other physical properties related to secondary porosity, such as lineaments and drainage (Elewa and Qaddah 2011 ). Here, the geological map and lithological (rock) data are obtained from 1:100,000 scale geological maps of the Geological Survey of Iran (GSI). The characteristics of lithology and geology (age) of the study area are presented in Table  1 .

The spatial distribution of geology (Fig.  4 A) and lithology (Fig.  4 B) shows that, geologically, the Goharkooh Plain watershed is located between the tectonic units of Nehbandan, Khash, Makran, Lut Block, and Central Iran (Aghanabati 2004 ). The existing rock outcrops in the study area can be structurally divided into two zones: the East Iran Flysch Zone and the Lut Zone associated with Central Iran. The study area consists of flysch deposits and colorful ophiolitic mixtures (Calcolith) of eastern and southeastern Iran. The major part of the Goharkooh Plain is formed of quaternary alluvial lithology titled current alluvial deposits. Quaternary deposits in the plain include coarse, medium, and fine alluvium. Coarse and medium particles are located in the central and eastern areas, while fine particles are in the western part of the plain and alluvial fans. These deposits provide a suitable bed for forming and nourishing the aquifer. After that, conglomerate formations have good potential for infiltration, but their spatial extent in the study area is severely limited. Impermeable and hard lithologies are mainly found in the rock outcrops of central and western parts of the study area, which have a low potential for groundwater.

Thematic layers of parameters: A geology; B lithology; C DEM; D slope; E drainage density; and F rainfall; extracted from GIS processing

DEM Topographic elevation likely influences the occurrence of groundwater and is regulated by various geomorphological and hydrogeomorphological processes (e.g., geology, meteorological conditions, land degradation, etc.) as outlined by (Pourghasemi and Beheshtirad 2015 ). Elevation indirectly signifies the role of influential factors on groundwater infiltration, such as slope, drainage network, cumulative flow, and soil. In higher elevations, primarily due to steeper slopes, the infiltration rate of rainwater and surface flows into the subsurface for aquifer recharge and enhancing groundwater potential is minimal. Specifically, in the study area, the minimal vegetation cover in the highlands, which are predominantly covered with hard rock formations, significantly reduces precipitation infiltration. On the other hand, higher elevations are sites of more significant snow and rain accumulation, thereby increasing groundwater recharge potential. Thus, the role of topography is heavily dependent on the geographical and climatic environment of the catchment areas. Due to thermal conditions, snowfall is rare in dry and semiarid regions, especially at lower latitudes.

Consequently, high elevations lacking snow accumulation do not significantly contribute to enhancing groundwater resources. Furthermore, due to steeper slopes, the drainage density is high in higher elevations, and rainwater is quickly converted to surface runoff, draining toward the lower plains and flat areas. The dry geographical environment conditions, along with scant vegetation cover, accelerate this process. Therefore, geographical environments' climatic and biotic conditions in mountainous and rugged areas affect their differing roles in groundwater potential.

The elevation range within the study area varies from 1207 to 3098 m. The average elevation of the study basin is 1350 m, meaning that a substantial portion of the study area is at an elevation lower than 1400 m, primarily encompassing the western part of the study area. Higher elevations are limited to the eastern part, where the dendritic drainage network in the Goharkooh Plain catchment area is formed. The topographic map of the study area (Fig.  4 C) has been developed using DEM extracted from ALOS PALSAR RTC data (Laurencelle et al. 2015 ).

Slope gradient In dry regions characterized by uneven topography, the slope gradient is recognized as a crucial factor influencing groundwater potential. This impact is exerted both directly and indirectly, affecting various elements such as precipitation, the drainage network, and cumulative flow. It is acknowledged that the infiltration rate of rainfall is predominantly determined by the slope gradient, making it a key factor in these environments (Selvam et al. 2014 ; Deepa et al. 2016 ). In areas with gentler slopes, the slower surface flow and increased water accumulation contribute to higher rates of groundwater infiltration. Conversely, the infiltration rate typically decreases in areas characterized by steeper slopes. This is because, on steep slopes, surface waters seldom have the chance to permeate the ground, except in cases where fractures, cracks, and faults provide pathways for infiltration. Furthermore, the slope plays a significant role in shaping the pattern and density of the drainage basin. The slope of the land is also a significant factor in determining groundwater's existence and flow patterns (Yeh et al. 2016 ).

The thematic slope layer, as depicted in Fig.  4 D, was derived from the raster data of DEM, which boasts a resolution of 12.5 m, sourced from ALOS PALSAR. The study area's slope ranges span from a flat zero degrees to a steep 75.15 degrees. Notably, steep slopes are found predominantly in the eastern regions of the study area, particularly in the Tafatan heights, as well as along the eastern and southeastern fringes. Conversely, the slope gradient is relatively gentle in the other parts of the plain. The catchment basin's outlet, located in the southwest corner of the plain, features the minimum slope observed in the study.

Drainage density The drainage network is a highly influential factor in the potential of groundwater, especially in drylands (Yeh et al. 2009 ). The drainage network reflects the cumulative water flow across the catchment surface and is intricately connected to the process of water infiltration into the subsurface. The density of this network is a critical determinant in ascertaining the potential of areas for groundwater recharge (Yeh et al. 2016 ). In varying geographical environments, lower-order streams within the drainage network, particularly those of orders 1 and 2, typically exhibit reduced infiltration due to their steeper slopes. In contrast, higher-order streams (orders above 3) demonstrate greater infiltration capabilities. Consequently, the contribution of lower-order streams to overall groundwater replenishment is often considered negligible. As a result, drainage density emerges as a critical factor influencing groundwater potential in dry areas with irregular topography. This density also indirectly reflects the role of precipitation in these environments.

The study area exhibits a dendritic drainage pattern. In such patterns, it is observed that drainage density diminishes in downstream areas as stream orders increase. The drainage density within this area ranges from zero to 8.6 km/km 2 . Notably, areas characterized by low drainage densities are typically flat and plain. This low-density pattern is exemplified by the mainstream flow, which predominantly runs in north–south and east–west directions, as illustrated in Fig.  4 E. Thus, according to the Strahler method (Strahler 1957 ), streams of orders 1 and 2 are excluded from consideration, focusing the analysis on streams of order 3 and higher for assessing drainage density.

The drainage density length \({D}_{\text{d}}({\text{km}}^{-1})\) is derived from the total drainage length divided by each area unit (Greenbaum 1985 ) using Eq. ( 1 ):

In this context, \({\sum }_{i=1}^{i=n}{S}_{i}\) represents the total length of the drainage network within the catchment area \((L)\) , while A denotes the unit area ( \({L}^{2}).\) The total length of the drainage density is closely correlated with the groundwater recharge rate. Specifically, regions exhibiting high drainage density significantly generate surface runoff, resulting in a reduced groundwater recharge volume. To construct the thematic layer representing drainage density, data were utilized from the drainage network ascertained from the DEM in conjunction with the topographic characteristics of the study area.

Precipitation Precipitation, particularly total annual rainfall, is essential for maintaining the hydrological balance within catchment basins and naturally recharging aquifers. Distinct forms of precipitation, including rain and snow, contribute variably to groundwater replenishment. Moreover, the intensity of precipitation profoundly impacts soil water infiltration, a critical process for groundwater recharge. Nonetheless, in small catchments situated in arid and semiarid regions, the negligible spatial variability of precipitation coupled with scant data often complicates the delineation of GWPZs. With the advancement of RS techniques, access to global satellite-based precipitation products like the GPM has improved. The GPM dataset, which spans from 1981 to the present, offers diverse temporal resolutions—daily, pentad, and monthly—and detailed spatial resolutions of 0.25 × 0.25 and 0.05 × 0.05 degrees, facilitating an extensive analysis of precipitation on a global scale (Funk et al. 2015 ). In this study, data with a resolution of 0.05 × 0.05 degrees were utilized.

In arid regions, exemplified by the Goharkooh Plain catchment, upland areas lack the capacity to sustain aquifers due to sparse vegetation, steep inclines, and low permeability. Conversely, the movement of rainwater from these uplands to lower slopes with reduced flow velocities—particularly in areas with conducive geological conditions—supports the formation of groundwater sources. The spatial distribution of rainfall within this catchment corresponds to its topographical diversity: flat and low-lying areas generally receive minimal precipitation, whereas higher elevations, such as those on the slopes of Taftan Peak (illustrated in Fig.  4 F), record more substantial rainfall. The annual precipitation across the catchment varies from 92 to 134 mm.

RS processes

Vegetation Cover Vegetation cover, in addition to influencing the infiltration of water into the ground and recharging groundwater aquifers, also prevents surface erosion and, by reducing surface flow, delays the concentration time. The normalized difference vegetation index (NDVI), a RS index, calculates vegetation cover by measuring the difference between the near-infrared spectrum (reflective of vegetation) and the red spectrum (absorptive of vegetation). The NDVI index can be determined from the surface reflectance images of Landsat 8 (L8SR) for identifying green vegetation. The Digital Number (DN) values from L8SR images are converted into reflectance values according to Eq. ( 2 ):

Sun angle correction is carried out using Eq. ( 3 ):

Subsequently, the vegetation cover index (NDVI) is calculated according to Eq. ( 4 ):

Here, NIR refers to the near-infrared band, and Red refers to the red band. NDVI values range from − 1 to + 1, although there is no precise range for a specific phenomenon. However, negative values of this index are likely to include water bodies, clouds, and snow. A range from 0 to 0.4 indicates sparse or no vegetation, while a range above 0.4 represents green and leafy vegetation (Akbar et al. 2019 ). This index effectively facilitates the extraction of vegetation density maps, land use maps, crop types, plant health, and many other aspects.

In dry and semiarid areas like the Goharkooh basin, natural vegetation is sparse and poor due to limited rainfall. Green vegetation is mainly related to irrigated agriculture on flat lands and gardening along waterways. The range of the vegetation cover index in the study area varies from -0.17 to 0.45 (Fig.  5 A). High and very high values of the vegetation index are observed in the lowlands and on alluvial fans dominated by agriculture and gardening. On the other hand, semi-dense vegetation covers, including shrubs and trees, are aligned with gardening uses along waterways.

Thematic layers of parameters: A NDVI; B LD; C LULC; extracted from RS processing

Lineament Density In a hard rock landscape, lineaments represent fault and fracture zones, leading to increased secondary porosity and permeability (Dinesh Kumar et al. 2007 ; Selvam et al. 2014 ). Lineament density is a good indicator of groundwater recharge, requiring RS analysis of fractures or structures (Yeh et al. 2009 ). Lineament density \({L}_{\text{d}} \left({\text{km}}^{-1}\right)\) is derived by dividing the total lineament length by unit area according to Eq. ( 5 ):

Here, \({\sum }_{i=1}^{i=n}{L}_{i}\) refers to the total lineament length \((L)\) and A refers to the unit are ( \({L}^{2}).\) . High lineament or fracture lengths per unit area indicate a high degree of fracturing and an area with high potential for groundwater recharge. The assessment of lineament density is based on a 1:100,000 scale geological map and a combination of bands 1, 4, and 8 of the Landsat 8 images, using directional filters.

The range of lineament densities in the study area varies from zero to 1.09 km/km 2 (Fig.  5 B). The central part of the study area is mainly characterized by hard rock outcrops, which, in addition to weathering, are marked by joints, cracks, fractures, and fault systems. The western regions of the study area also align with the direction of regional faults. Since a large part of the study area is covered by flat plains with recent alluvial formations, dense lineament areas are limitedly manifested in the central and peripheral regions of the study area.

LULC Land use and land cover are significant factors influencing the recharge of groundwater (Kaewdum and Chotpantarat 2021 ). Leduc et al. (2001 ) found that one of the main contributors to groundwater recharge volume is related to changes in land use. Bhave et al. (2019 ) and Shaban et al. (2006 ) also concluded that land cover influences groundwater recharge. LULC patterns provide information about infiltration and runoff controlled by the nature of surface materials. LULC data in this study were extracted from six bands of Sentinel-2 surface reflectance data (Karra et al. 2021 ).

LULC patterns such as cultivated and fallow lands, bushlands and pastures, and scattered trees, poor pastures, settlements, and rocky outcrops exist in the study area (Fig.  5 C). In the plain area, the primary land use and cover are agricultural lands, and in the foothills, there is a dominance of poor pastures and bushlands. In agricultural lands, due to plowing operations, water infiltration is higher. Areas with dense bushlands and rich pastures, characterized by relatively high vegetation density, exhibit significant potential for water infiltration as they effectively reduce surface water flow.

Step 3: weighting the thematic layers using AHP analysis

The AHP is a subset of the MCDM method, involving the analysis and decision-making related to multiple objectives (Cay and Uyan 2013 ). It is widely used for spatial decision-making in natural resource management, including groundwater issues. AHP is a mental approach where the selection of subclasses and weight allocation are based on comparisons among various criteria derived from appropriate decision strategies. In MCDM analysis, weights are assigned to each influencing factor, considering its role in a specific region (Agarwal and Garg 2016 ; Jhariya et al. 2017 ). AHP methodology involves calculating weights from a preference matrix that represents map layers. Weights are created by comparing related criteria based on preferential factors. The ability to handle a large amount of heterogeneous data in a forward and clear manner, based on weights, makes this method one of the most popular approaches in different GIS systems (Feizizadeh and Blaschke 2012 ; Khan and Jhariya 2019 ). The inference of weights for the main thematic layers and their subclasses is based on literature review (Agarwal et al. 2013 ; Jhariya et al. 2016 ; Murmu et al. 2019 ), opinions of hydrogeology experts (who have a deep understanding of the region's groundwater tables), and field knowledge, using the 1 to 9 Saaty scale (Saaty 2004 , 2008 ).

After comparing each layer based on their relative importance, a pairwise comparison matrix for the nine variables (in this case, DEM, slope, drainage density, lineament density, LULC, geology, lithology, vegetation cover, and rainfall) is constructed (Table 2 ). To calculate the normalized weights, the sum of values for each column is estimated using Equation ( 6 ) and shown at the bottom of the table.

Here, \({L}_{ij}\) is the sum of values for each column of the pairwise comparison matrix, and \({C}_{ij}\) is the variable used for analysis.

In AHP analysis, a comparison of the considered criteria in terms of number (n) must be performed, and a square matrix of \(A=\left(aij\right)\) is created. For this purpose, all values of each column are divided by the sum of that column through Eq. ( 7 ) to create a normalized pairwise comparison matrix. The normalized weight (N \(wt\) ) of each variable is obtained by averaging all values for each row of the normalized pairwise comparison matrix (Table  3 ). The sum of normalized weights is always one. The highest weight, similar to the study by (Mallick et al. 2019 ), was attributed to lithology, and the lowest weight was allocated to the rainfall variable.

During the application of the AHP method, a certain level of inconsistency may arise from subjective or mental judgments. To assess accuracy, the consistency ratio (CR) must be calculated. To this end, the statistics of eigenvalue and eigenvector were calculated from Eqs. ( 8 ) and ( 9 ), respectively, for the weights assigned to the nine thematic layers and their subclasses. First, each column of the pairwise comparison matrix is multiplied by the weight of the corresponding variable. Then, the average weight value is obtained through the sum of rows. Dividing the total weight value by the weight of each variable gives the value of ( \(\uplambda )\) . The maximum eigenvalue \({(\lambda }_{\text{max}}\) ) is calculated through Eq. ( 9 ), which is 10.1 in this case.

Here, \(W\) is the eigenvector, a is the eigenvalue of criterion \(i\) , and \({\lambda }_{max}\) is the eigenvalue of the pairwise comparison matrix.

After that, the uncertainty judgment is obtained based on Saaty's consistency index (CI), calculated through Equation ( 10 ). The CI value in this work is 0.13.

Here, \(n\) represents the number of criteria or subclasses.

Finally, the consistency ratio (CR) is obtained by dividing the consistency index by the random index ( \(\text{RI}\) ). The consistency ratio (CR) of a pairwise matrix is calculated through Eq. ( 11 ):

Here, \(\text{RI}\) is the random index. \(\text{RI}\) values represent different numbers from n , as shown in Table 4 .

The CR value must be less than or equal to 0.1(Maity and Mandal 2019 ). Otherwise, if the consistency ratio is equal to or greater than 0.1, a review of judgments must be undertaken. Otherwise, the AHP analysis may produce flawed results (Chakraborty and Banik 2006 ). Considering the nine variables and referring to Table  3 , the value of the random index or \(RI\) is 1.45. Therefore, the consistency ratio in this case is 0.094, indicating acceptable inconsistency.

Weighted Overlay Analysis

After internal weighting of each thematic layer and prioritizing modeling criteria relative to the integration of selected thematic maps for the preparation of GWPZs, Equation ( 12 ) was applied in a GIS environment.

Here, \({w}_{i}\) and \({w}_{j}\) are the normalized weights of the \({i}\) th and j th classes of thematic layers, respectively. \(m\) represents the total number of thematic layers, and \(n\) denotes the total number of subclasses in each thematic layer. Finally, higher values resulting from this equation indicate greater potential for the presence of groundwater (Malczewsk 1999 ; Agarwal and Garg 2016 ).

Step 4: validation and mapping of potential groundwater zones

At the end of the study, the predicted potential groundwater zones are validated in relation to the data collected from the existing exploitation wells (485 active wells) in the region. The analysis of the ROC curve is a standard technique for assessing the accuracy of a diagnostic test. The ROC curve plots false positive rates (FPR) on the horizontal axis (X-axis) and true positive rates (TPR) on the vertical axis (Y-axis) (Pradhan 2013 ). The area under the curve (AUC) of the ROC indicates the accuracy of a prediction process by explaining the system's ability to anticipate the precise occurrence or non-occurrence of predefined events. A curve with the largest AUC represents the best method.

Naghibi et al. (2015 ) summarized the relationship between the AUC and the accuracy of prediction as follows: excellent (0.9 to 1), very good (0.8 to 0.9), good (0.7 to 0.8), average (0.6 to 0.7), and poor (0.5 to 0.6). This curve is generated by plotting the TPR, also known as the sensitivity, against FPR, also known as 1-specificity. In conventional methodology, the FPR and TPR are displayed on the respective axes (Equations 13 and 14 ).

The AUC of the ROC chart is used as a metric to measure the accuracy of the model's predictions. This chart assesses the comparison between the performance in terms of false positive percentage and true positive percentage. The true positive percentage indicates instances where the expected yield from the prediction map matches the actual yield. On the other hand, the false positive percentage represents the percentage of incorrectly diagnosed positive cases.

Results and discussion

The analytical process applied in this study culminated in insightful findings regarding the groundwater potential in the Goharkooh Plain. Subsequent to the weight assessment conducted using the AHP, each thematic raster layer underwent standardization and reclassification. Aligning with the methodologies employed in recent studies, such as those by (Ndhlovu and Woyessa 2021 ; Khan et al. 2022 ), a ranking value system ranging from 1 to 5 was adopted. These rankings correspond to very low, low, moderate, high, and very high categories for each thematic layer, respectively. Table 5 presents the normalized weights assigned to each thematic layer and the normalized ranks allocated to each subclass within these layers. As per the data in the table, the lithology layer was assigned the highest weight (0.28), while the rainfall layer was designated the lowest weight (0.01). Weights for the internal subclasses of each layer varied between 0.33 and 0.07, reflective of their respective direct or inverse correlations with groundwater permeability and recharge capacities.

Spatial distribution of thematic layers

Utilizing the AHP, it was determined that the lithology thematic layer holds a substantial weight of 28% (as shown in Table  5 ), rendering it the most significant factor influencing groundwater potential in the study area. Given the direct correlation between lithology and permeability, classes exhibiting higher permeability were assigned elevated ranks, and conversely for less permeable classes. This led to the reclassification of the lithology thematic layer into five distinct groups, thereby generating a revised lithology layer reflective of varying permeability levels. This layer's highest ranking (Class 5) was attributed to the Quaternary (Q) sedimentary deposits, as depicted in Fig.  6 A.

Reclassify maps of: A lithology; B geology; C drainage density; D slope; E DEM; F NDVI; G lineament density; H LULC; and I rainfall

The study revealed that lithologies from the Pliocene, Miocene, and Eocene geological epochs, often composed of conglomerates, exhibit good to moderate permeability. Conversely, lithologies dating back to the Paleocene, Cretaceous, Oligocene, and Permian epochs, characterized frequently by a composition of rich mixtures and hard rocks, demonstrate poor permeability, as supported by (Mallick et al. 2019 ). Consequently, the geology (age) thematic layer was assigned a substantial weight of 23% (refer to Table  5 ). Within this layer, the subclasses—namely Quaternary and Pliocene, Miocene, Eocene, Paleocene, and the combined group of Cretaceous, Oligocene, and Permian—were ranked on a scale from very good to very poor, corresponding to numerical values ranging from 5 to 1, as illustrated in Fig.  6 B.

• Drainage density

Within the Goharkooh watershed, the drainage density thematic layer was ascribed a significant weight of 15% (as indicated in Table  5 ), positioning it as the region's third most influential factor for groundwater potential. Owing to the inverse relationship between drainage density and surface water accumulation, areas with lower drainage densities were assigned higher ranks. Accordingly, the subclasses within this layer, categorized as (0–2), (2–4), (4–6), (6–8), and (8–10), were ranked on a scale from very good to very poor. This ranking system corresponds to numerical values ranging from 5 to 1, as depicted in Fig.  6 C.

Slope gradient

In assessing the Goharkooh watershed, the slope gradient thematic layer was allocated a weight of 12% (refer to Table  5 ), marking it as the fourth most significant factor influencing groundwater potential. Given the inverse relationship between slope gradient and surface water accumulation, areas with gentler slopes received higher rankings. Consequently, the subclasses within this layer, defined as (0–3), (3–2), (3–5), (5–8), and (8–10), were assigned ranks from very good to very poor. This ranking corresponds to a numerical range from 5 to 1, as illustrated in Fig.  6 D.

The DEM thematic layer has been identified as the fifth most critical factor affecting groundwater potential in the Goharkooh watershed, with an assigned weight of 11% as per Table  5 . According to Arulbalaji et al. ( 2019 ), there exists an inverse correlation between surface water accumulation, drainage density, and DEM and a direct correlation with slope (Arulbalaji et al. 2019 ). This relationship necessitates assigning higher weights to lower elevations and lower weights to higher elevations. Accordingly, the subclasses within this layer, categorized as (1200–1250), (1250–1300), (1300–1400), (1400–1600), and (> 1600), have been ranked from very good to very poor. These rankings correspond to a numerical range from 5 to 1, as depicted in Fig.  6 E.

Vegetation cover

The vegetation covers the thematic layer, ranking sixth, received a weight of 5% (Table  5 ). The NDVI was used to categorize vegetation density into five subclasses. These subclasses range from very poor potential, corresponding to a very low NDVI range, to very high potential, associated with a high NDVI range. This classification is denoted by a numerical range of 1 to 5, as shown in Fig.  6 F. Notably, most of the study area is classified with a very low rank in terms of vegetation cover.

Lineament density

The lineament density thematic layer was assigned a weight of 3% in the groundwater potential of the study area (Table  5 ). The reclassified lineament density map delineates the area into five distinct classes, as depicted in Fig.  6 G. These classes range from high potential, corresponding to very high lineament density, to very low potential, associated with very low lineament density. The rankings for these classes span from 5 to 1, with higher-density subclasses receiving higher ranks. Consequently, the subclasses (0.8–1.09), (0.6–0.8), (0.4–0.6), (0.2–0.4), and (0–0.2) are, respectively, assigned ranks from very good to very poor.

In the overall assessment of groundwater potential for the Goharkooh watershed, the LULC criterion has been assigned a weight of 2%, as indicated in Table  5 . Subsequently, the reclassified LULC map categorizes the area into five distinct classes. These classes are arranged according to the potential for water infiltration, ranging from very high to very low potential. The ranking system for these classes is denoted by numerical values from 5 to 1, as illustrated in Fig.  6 H.

Precipitation

The weighting and grading of annual precipitation amounts are based on the method by (Khan et al. 2022 ), where higher weights are assigned to higher precipitation amounts and vice versa (F i g.  6 I). The spatial distribution layer of total annual precipitation in the Goharkooh watershed, with a weight of 1% (Table  5 ), has the least contribution to groundwater potential assessment. However, as noted by researchers such as (Uc Castillo et al. 2022 ) and (Abdullateef et al. 2021 ), rainfall is the main hydrological source of groundwater recharge in semiarid regions.

A weighted overlay analysis was conducted to delineate GWPZs, incorporating the relative significance of various thematic layers and their respective subclasses. During the overlay analysis, the weight assigned to each thematic layer is multiplied by the rank of its subclasses, subsequently integrating these products across all layers. The specific weights of the thematic layers and their subclasses are detailed in Table  5 . The groundwater potential index (GWPI), derived from Eq. ( 12 ), is presented in the groundwater potential zoning map (Fig.  7 ). This aggregation of the nine influential factors results in the classification of the GWPZs into five distinct categories: very good, good, moderate, poor, and very poor potential. The 'very good' potential zone is characterized by favorable lithological and geological conditions, coupled with low drainage density and gentle slopes, which enhance infiltration capabilities, particularly in the flat and downstream segments of the basin. Table 6 , which presents the area percentages for each groundwater potential class, indicates that this zone constitutes approximately 20% of the total study area.

GWPZs in the Goharkooh basin

Zones with very good and good groundwater potential, covering about 19.98% and 20.45% of the area, respectively, are located in geological formations of current alluvial deposits and conglomerates. These zones, characterized by geomorphological units of alluvial plains and alluvial fan plains, are further influenced by factors such as low drainage density, low slope and elevation, vegetation cover density, and agricultural land use. These conditions provide ideal circumstances for water infiltration into the subsurface, thereby enhancing and developing groundwater resources. Conversely, the eastern areas of the basin, due to high drainage density influenced by high elevation and slope along with predominantly impermeable lithology, exhibit very poor groundwater potential, as indicated in Fig.  7 . Similarly, the western peripheral areas of the study region display very poor groundwater potential due to the dominance of colorful mixture lithologies with low permeability. Collectively, zones with moderate, poor, and very poor potential comprise approximately 20.19%, 20.85%, and 18.53% of the Goharkooh watershed area, respectively. Overall, the study area predominantly exhibits very good, good, and moderate potential for groundwater, indicating a need for integrated water management strategies that address domestic water supply, environmental water needs, agriculture, and industry.

Validation of groundwater potential map

A dual-phase validation approach was implemented to evaluate the effectiveness of the groundwater potential zoning methodology developed in this study. The initial phase encompassed a visual comparison between the groundwater potential map and the spatial distribution of wells within the Goharkooh basin, as depicted in Fig.  7 . This comparison aimed to verify the reliability of the results obtained. A notable alignment between the distribution of existing wells and areas designated as having high groundwater potential suggested a commendable degree of accuracy in the generated map. This inference is based on the rationale that a higher concentration of productive wells serves as a robust indicator of significant groundwater availability in those zones. Of the 480 observation wells in the Goharkooh basin, 306 were in zones classified as having excellent potential, while 111 were located in areas deemed to have good potential. Additionally, the discharge rates of these wells were subject to scrutiny. Among the 393 wells with documented discharge rates, the minimum, maximum, and average discharge rates recorded were 0.3 L per second, 53 L per second, and 13.8 L per second, respectively. It was observed that 36% of these wells exhibited discharge rates surpassing the average, whereas 64% fell below the average. Wells with above-average discharge rates predominantly occurred in areas identified as having excellent potential, while those with lower rates were typically found in zones of good potential.

In applying the ROC curve method, AUC is an essential metric for assessing the model's accuracy, ranging from 0.5 to 1. In this study, a representative dataset was compiled by selecting random pixels from locations with and without wells. This dataset facilitated the evaluation of the model's capability to accurately predict groundwater potential areas, as shown in Fig.  8 . The AUC value obtained for the applied methodology was 0.815, indicating a very good level of agreement, quantified at 81.5%, between the existing groundwater data and the identified potential groundwater zones. Such results underscore the efficacy of the weighted overlay analysis method as a reliable estimator of well performance and sensitivity within the Goharkooh watershed.

Validation of the results of GWPZs

In this research, RS techniques, GIS, and MCDM were adeptly utilized in a structured four-stage process to assess GWPZs in the Goharkooh Plain, situated in the southeastern part of Iran's driest drainage basin, the Lut Desert. The process entailed comprehensive data collection, meticulous RS & GIS processing, the derivation of weights using the AHP, and an overlay analysis to identify GWPZs, culminating in model validation. The challenge posed by the scarcity of regional data on crucial thematic layers was addressed by strategically using satellite imagery and RS processing techniques. The application of the AHP method for criteria evaluation demonstrated that the nine selected factors—lithology, geology, drainage density, slope gradient, DEM, vegetation cover index, lineament density, LULC, and annual rainfall—contributed 28%, 23%, 15%, 12%, 11%, 5%, 3%, 2%, and 1%, respectively, to the groundwater potential modeling. A CR of 0.094 was achieved, indicating an acceptable degree of consistency in the expert judgments concerning the weights of these criteria and their subclasses in accordance with the AHP methodology.

The results of the weighted overlay index were classified into five distinct potential categories: very high, high, moderate, low, and very low. It was found that zones with very high groundwater potential, encompassing less than 20% of the study area, were primarily located in low-lying areas characterized by minimal drainage density and gentle slopes, particularly around recent alluvial deposits. This is further evidenced by the dense concentration of agricultural activities and significant exploitation of groundwater resources, as indicated by the high number of operational wells in these areas. Conversely, areas with high and moderate groundwater potential, constituting 20.45% and 20.19% of the basin area, exhibited a sparser distribution. These zones are generally situated on the peripheries of the very high potential areas and in the foothills, a pattern highlighted by the scattered rural settlements engaged in orcharding on the eastern slopes of the Goharkooh basin. The remaining zones, characterized by impermeable rock outcrops, steep terrain, sparse vegetation, and high drainage density, were identified as having poor to very poor groundwater potential. These areas, covering 20.85% and 18.53% of the basin area, respectively, are predominantly found in the eastern, central, and western edges of the watershed. A visual analysis of the spatial distribution of groundwater extraction wells within the basin revealed that 60% of the wells are located in the very high potential zone, with 20% in the high potential zone and the rest in other potential zones. The drilling of 40 wells deeper than 50 m, each with a discharge rate exceeding 20 L per second in areas of very high groundwater potential, substantiates the accuracy of the groundwater potential mapping in the Goharkooh Plain. Additionally, the quantitative validation of the groundwater potential prediction map, employing the ROC curve with an AUC value of 0.815, corroborates the high predictive accuracy of the study.

This research offers substantial contributions to the effective management of groundwater resources. The delineation of zones according to their groundwater potential—ranging from very low to very high—paves the way for formulating targeted strategies to mitigate groundwater contamination and enhance resource management practices. Effective stewardship of existing underground aquifers and similar ecosystems is crucial to fulfill the water requirements of the target region in the foreseeable future, prevent further depletion of these vital resources, and promote sustainable development. The conceptual model employed in this study has proved to be adept at addressing the specific objectives of this research, demonstrating its efficacy and applicability in the field of groundwater management. Furthermore, considering the nearly equivalent significance and influence of certain criteria, such as geology and lithology, which exhibit identical spatial distributions, the integration of these layers is recommended as a potential avenue for future research.

Data availability

The data presented in this study are available upon corroborated request from the corresponding author.

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H.N. was involved in conceptualization and investigation; H.N., M.S. and V.S. were responsible for methodology and writing-original draft preparation; H.N. and M.S. helped with software and validation; V.S. and M.M.R. contributed to formal analysis and writing-reviewing and editing; M.S. took part in visualization; and H.N. and M.M.R. participated in supervision. All authors have read and agreed to the published version of the manuscript.

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Nazaripour, H., Sedaghat, M., Shafaie, V. et al. Strategic assessment of groundwater potential zones: a hybrid geospatial approach. Appl Water Sci 14 , 185 (2024). https://doi.org/10.1007/s13201-024-02243-x

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A Review of Subsidence Monitoring Techniques in Offshore Environments

• Thomas, Frank
• Livio, Franz A.
• Ferrario, Francesca
• Pizza, Marco
• Chalaturnyk, Rick

In view of the ever-increasing global energy demands and the imperative for sustainability in extraction methods, this article surveys subsidence monitoring systems applied to oil and gas fields located in offshore areas. Subsidence is an issue that can harm infrastructure, whether onshore or especially offshore, so it must be carefully monitored to ensure safety and prevent potential environmental damage. A comprehensive review of major monitoring technologies used offshore is still lacking; here, we address this gap by evaluating several techniques, including InSAR, GNSSs, hydrostatic leveling, and fiber optic cables, among others. Their accuracy, applicability, and limitations within offshore operations have also been assessed. Based on an extensive literature review of more than 60 published papers and technical reports, we have found that no single method works best for all settings; instead, a combination of different monitoring approaches is more likely to provide a reliable subsidence assessment. We also present selected case histories to document the results achieved using integrated monitoring studies. With the emerging offshore energy industry, combining GNSSs, InSAR, and other subsidence monitoring technologies offers a pathway to achieving precision in the assessment of offshore infrastructural stability, thus underpinning the sustainability and safety of offshore oil and gas operations. Reliable and comprehensive subsidence monitoring systems are essential for safety, to protect the environment, and ensure the sustainable exploitation of hydrocarbon resources.

• subsidence monitoring;
• structural integrity;
• seafloor deformation;
• integrated monitoring approaches