Dynamic and Scalable Flood Risk Assessment Using GIS, AHP, and Novel Fuzzy AHP: A Case Study of the Upper Tigris Basin

Dynamic and Scalable Flood Risk Assessment Using GIS, AHP, and Novel Fuzzy AHP: A Case Study of the Upper Tigris Basin | 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 Dynamic and Scalable Flood Risk Assessment Using GIS, AHP, and Novel Fuzzy AHP: A Case Study of the Upper Tigris Basin Berfin Kaya, Recep Çelik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5776107/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 Floods are one of the most frequent and destructive natural disasters, requiring advanced methodologies for effective risk assessment and management. This study evaluates flood risk in the Upper Tigris Basin using Geographic Information Systems (GIS) integrated with the Analytic Hierarchy Process (AHP) and Fuzzy AHP (FAHP). By analyzing eight critical parameters—precipitation, slope, elevation, lithology, land use, soil type, aspect, and proximity to rivers—risk maps were generated through Multi-Criteria Decision-Making (MCDM) techniques. The AHP method identified precipitation as the most critical factor, contributing 32.5% to overall risk, while FAHP refined the results by addressing uncertainties, enhancing the weights of key parameters like precipitation (32.6%) and slope (15.4%). The FAHP map showed greater differentiation in medium- and low-risk areas, improving its suitability for localized interventions. High-risk zones in northeastern districts, including Lice, Hani, and Hazro, were influenced by high precipitation and steep slopes. Beyond the local context, this study demonstrates the global applicability of integrating GIS with fuzzy logic-based methods for disaster risk assessment. By effectively capturing uncertainties and enhancing precision, these methods provide a replicable framework for other regions facing similar challenges. This research contributes to the scientific understanding of flood dynamics and offers innovative tools for improving disaster preparedness and mitigation strategies worldwide. Upper Tigris Basin Flood Risk Analysis Multi-Criteria Decision-Making methods Analytical Hierarchy Process Fuzzy Analytical Hierarchy Process Full Text Additional Declarations No competing interests reported. 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. 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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-5776107","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398891018,"identity":"d2c81f66-5025-4102-b0b1-0e6516ec7bee","order_by":0,"name":"Berfin Kaya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACAyD+2AAk+NkbEh8AaR4+IrQwzgRpkew58Bikn4eNaC0GNxKfSYBECGoxZ+992Dij5rA8w4HktMqvOXYybAzMDx/dwKPFsue4YeOGY4cNGxuOpd2W3ZYMdBibsXEOPofdSGN/+IDtMGMzY0/abcltzEAtPGzSeLXcf8bY+ODfYfs2Zv5vxZLb6onQcoONsXFj2+HEHjaGNMaP2w4T1mLZk8bYOLMvPXkGD0OyNOO24zxszAT8Ys5+jLGx55u17f77DxI//txWbc/P3vzwMT4tUNAMJpl5wCRh5SBQByYZfxCnehSMglEwCkYYAAC6GU5DTN4adgAAAABJRU5ErkJggg==","orcid":"","institution":"Dicle University","correspondingAuthor":true,"prefix":"","firstName":"Berfin","middleName":"","lastName":"Kaya","suffix":""},{"id":398891019,"identity":"d01da5d8-7e54-42b1-92fa-8aea902715cf","order_by":1,"name":"Recep Çelik","email":"","orcid":"","institution":"Dicle University","correspondingAuthor":false,"prefix":"","firstName":"Recep","middleName":"","lastName":"Çelik","suffix":""}],"badges":[],"createdAt":"2025-01-06 19:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5776107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5776107/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73567775,"identity":"5a314751-0109-4378-808c-fd3f1126aa63","added_by":"auto","created_at":"2025-01-11 14:53:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1012871,"visible":true,"origin":"","legend":"","description":"","filename":"stochasticBERFNKAYA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5776107/v1_covered_b12114e0-7f37-47d6-a1fe-7b1008bf7a9d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic and Scalable Flood Risk Assessment Using GIS, AHP, and Novel Fuzzy AHP: A Case Study of the Upper Tigris Basin","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":"Upper Tigris Basin, Flood Risk Analysis, Multi-Criteria Decision-Making methods, Analytical Hierarchy Process, Fuzzy Analytical Hierarchy Process","lastPublishedDoi":"10.21203/rs.3.rs-5776107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5776107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFloods are one of the most frequent and destructive natural disasters, requiring advanced methodologies for effective risk assessment and management. This study evaluates flood risk in the Upper Tigris Basin using Geographic Information Systems (GIS) integrated with the Analytic Hierarchy Process (AHP) and Fuzzy AHP (FAHP). By analyzing eight critical parameters\u0026mdash;precipitation, slope, elevation, lithology, land use, soil type, aspect, and proximity to rivers\u0026mdash;risk maps were generated through Multi-Criteria Decision-Making (MCDM) techniques. The AHP method identified precipitation as the most critical factor, contributing 32.5% to overall risk, while FAHP refined the results by addressing uncertainties, enhancing the weights of key parameters like precipitation (32.6%) and slope (15.4%). The FAHP map showed greater differentiation in medium- and low-risk areas, improving its suitability for localized interventions. High-risk zones in northeastern districts, including Lice, Hani, and Hazro, were influenced by high precipitation and steep slopes. 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