Integrated Micro-Watershed Prioritization Using a Novel Multi-Criteria Decision-Making Approach

Integrated Micro-Watershed Prioritization Using a Novel Multi-Criteria Decision-Making Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Micro-Watershed Prioritization Using a Novel Multi-Criteria Decision-Making Approach Sudipto Halder, Santanu Banerjee, Somnath Mandal, Zarkheen Mukhtar, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7554723/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Watershed management is necessary to supply water resources and manage soil erosion, especially in semi-arid areas where land degradation and groundwater loss are serious issues. In the current research, morphometric analysis is combined with multi-criteria decision-making (MCDM) methods to prioritize micro-watersheds in the Bandu sub-watershed of Purulia district, West Bengal, India. The region is characterized by rolling terrain, hard rock, and semi-arid climate with extreme water scarcity and soil erosion. 13 morphometric parameters like Stream Length Ratio, Bifurcation Ratio, Drainage Density, Stream Frequency, and Hypsometric Integral were derived from Shuttle Radar Topography Mission (SRTM) Digital Elevation Models (DEMs). Micro-watersheds were divided using Principal Component Analysis (PCA) and K-means cluster classification according to the geomorphological differences. TOPSIS, COPRAS, and VIKOR, three various MCDM models, were utilized in ranking micro-watersheds using Fuzzy Shannon Entropy in weighing of the parameters. Each MCDM model was applied to assign priority ranks to micro-watersheds based on integrated performance across groundwater potentiality and soil erosion risk. The resulting rankings were then compared with an unsupervised classification generated using K-means clustering, which objectively groups watersheds with similar hydro-environmental characteristics. The comparison revealed a strong alignment between the TOPSIS model and the K-means derived clusters, particularly in micro-watersheds such as 2A2B5k and 2A2B5m, both of which received similar ranks in both approaches. To further evaluate model performance classification metrics were computed. The TOPSIS model emerged as the most consistent and accurate, achieving an accuracy of 0.73, outperforming both VIKOR and COPRAS. This consistency between supervised (MCDM) and unsupervised (K-means) methods strengths confidence in prioritization outputs. Micro-watershed Prioritization MCDM TOPSIS VIKOR CORPAS Full Text Additional Declarations Tables 1 to 14 are available in the Supplementary Files section. Supplementary Files tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7554723","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523418261,"identity":"1bcd19aa-5cf6-4bfe-a53e-2823895cf08a","order_by":0,"name":"Sudipto Halder","email":"","orcid":"","institution":"Jadavpur University","correspondingAuthor":false,"prefix":"","firstName":"Sudipto","middleName":"","lastName":"Halder","suffix":""},{"id":523418262,"identity":"1158bae7-8fae-4c7e-83bc-d7939deb73d2","order_by":1,"name":"Santanu Banerjee","email":"","orcid":"","institution":"University of 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Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Micro-watershed, Prioritization, MCDM, TOPSIS, VIKOR, CORPAS","lastPublishedDoi":"10.21203/rs.3.rs-7554723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7554723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWatershed management is necessary to supply water resources and manage soil erosion, especially in semi-arid areas where land degradation and groundwater loss are serious issues. In the current research, morphometric analysis is combined with multi-criteria decision-making (MCDM) methods to prioritize micro-watersheds in the Bandu sub-watershed of Purulia district, West Bengal, India. The region is characterized by rolling terrain, hard rock, and semi-arid climate with extreme water scarcity and soil erosion. 13 morphometric parameters like Stream Length Ratio, Bifurcation Ratio, Drainage Density, Stream Frequency, and Hypsometric Integral were derived from Shuttle Radar Topography Mission (SRTM) Digital Elevation Models (DEMs). Micro-watersheds were divided using Principal Component Analysis (PCA) and K-means cluster classification according to the geomorphological differences. TOPSIS, COPRAS, and VIKOR, three various MCDM models, were utilized in ranking micro-watersheds using Fuzzy Shannon Entropy in weighing of the parameters. Each MCDM model was applied to assign priority ranks to micro-watersheds based on integrated performance across groundwater potentiality and soil erosion risk. The resulting rankings were then compared with an unsupervised classification generated using K-means clustering, which objectively groups watersheds with similar hydro-environmental characteristics. The comparison revealed a strong alignment between the TOPSIS model and the K-means derived clusters, particularly in micro-watersheds such as 2A2B5k and 2A2B5m, both of which received similar ranks in both approaches. To further evaluate model performance classification metrics were computed. The TOPSIS model emerged as the most consistent and accurate, achieving an accuracy of 0.73, outperforming both VIKOR and COPRAS. This consistency between supervised (MCDM) and unsupervised (K-means) methods strengths confidence in prioritization outputs.\u003c/p\u003e","manuscriptTitle":"Integrated Micro-Watershed Prioritization Using a Novel Multi-Criteria Decision-Making Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 15:08:50","doi":"10.21203/rs.3.rs-7554723/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80aee1ad-a6e9-4e13-8a16-35732d8b3ad6","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T15:05:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 15:08:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7554723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7554723","identity":"rs-7554723","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}