Flood risk assessment based on machine learning and multi-criteria decision analysis

Flood risk assessment based on machine learning and multi-criteria decision analysis | 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 Article Flood risk assessment based on machine learning and multi-criteria decision analysis Guoyi LI, SHAO Weiwei, ZHAO Yang, Yong LI, Yi ZHANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9004660/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 In this study, three machine learning algorithms, including Multilayer perceptron (MLP), Radial basis function (RBF) and Back propagation (BP), to analyse the flood susceptibility of the study area. Considering the flood susceptibility, hazard and exposure of the study area, the Multi-Criteria Decision Analysis (MCDA) was used to conduct a flood risk assessment of the study area. Collection of information on the physical geography and socio-economic distribution of the study area, including Rainfall, DEM, Slope, Land use/Land cover (LULC), Distance from the rivers, Curve number (CN), Manning coefficient, topographic wetness index (TWI), population density and Gross Domestic Product (GDP) distribution. A machine learning approach to assess flood susceptibility in the study area, using six standard statistical indices (accuracy, precision, recall, F-score, Root Mean Square Error and Kappa index) and Receiver Operating Characteristic (ROC) curves called AUG to evaluate the performance of the machine learning model. The results show that all the proposed models perform well, but performance of the MLP model is the best (AUC = 0.951). Using the Analytic Hierarchy Process (AHP) method to determine the weights, the flood susceptibility map obtained from the MLP is overlaid with hazard and vulnerability for flood risk assessment. The results show that integrated machine learning algorithms and MCDA for flood analysis are a promising approach to flood risk assessment. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Flood risk assessment Machine learning Flood susceptibility Qianshan River Basin 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-9004660","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":606129646,"identity":"8fef3b7f-4ba1-4d01-8919-95b5bcbfc299","order_by":0,"name":"Guoyi LI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACNvbmgw8+VPznATIOEKeFj+dYsuGMM8wyQEYCcVrkJHLUpDnbmG3kJHwMiHSYRA6bNMMZNh42CZ6PN94w2MnpNhDSwvP2sHVBBQ8Pm3TvZss5DMnGZgcIaWHPS7w944wED5vM2W3SPAwHErcR1MKQYyDN22YAdFjOMyK1cOQYAbUk8IA9RZwWSCAf4AEyjC3nGBDhF/l2cFQesAcyHt54U2EnR1ALCpDgITJqkLWQqmMUjIJRMApGBAAAA/49qJ+hew8AAAAASUVORK5CYII=","orcid":"","institution":"Shandong Agriculture and Engineering University","correspondingAuthor":true,"prefix":"","firstName":"Guoyi","middleName":"","lastName":"LI","suffix":""},{"id":606129649,"identity":"a0764666-1ad7-4804-b491-7a602b5563c3","order_by":1,"name":"SHAO Weiwei","email":"","orcid":"","institution":"China Institute of Water Resources and Hydropower Research","correspondingAuthor":false,"prefix":"","firstName":"SHAO","middleName":"","lastName":"Weiwei","suffix":""},{"id":606129651,"identity":"67c75ff2-ae2c-4525-b639-e2fb3e2f08ac","order_by":2,"name":"ZHAO Yang","email":"","orcid":"","institution":"Shanghai Nuclear Engineering Research \u0026 Design Institute Co.,LTD","correspondingAuthor":false,"prefix":"","firstName":"ZHAO","middleName":"","lastName":"Yang","suffix":""},{"id":606129653,"identity":"5a5f26bf-c3af-4c43-867a-fe7899119b87","order_by":3,"name":"Yong LI","email":"","orcid":"","institution":"Shandong Agriculture and Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"LI","suffix":""},{"id":606129654,"identity":"09b42596-0e8b-48dd-9961-422a855b339f","order_by":4,"name":"Yi ZHANG","email":"","orcid":"","institution":"Shandong Agriculture and Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"ZHANG","suffix":""}],"badges":[],"createdAt":"2026-03-02 01:54:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9004660/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9004660/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109249455,"identity":"8311e22f-0199-44aa-b701-90eb75111990","added_by":"auto","created_at":"2026-05-14 08:53:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1200101,"visible":true,"origin":"","legend":"","description":"","filename":"manuscripts0308.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9004660/v1_covered_c9766c8b-326a-4ac1-8be6-5c51a0c2f2f7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Flood risk assessment based on machine learning and multi-criteria decision analysis","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":"Flood risk assessment, Machine learning, Flood susceptibility, Qianshan River Basin","lastPublishedDoi":"10.21203/rs.3.rs-9004660/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9004660/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, three machine learning algorithms, including Multilayer perceptron (MLP), Radial basis function (RBF) and Back propagation (BP), to analyse the flood susceptibility of the study area. Considering the flood susceptibility, hazard and exposure of the study area, the Multi-Criteria Decision Analysis (MCDA) was used to conduct a flood risk assessment of the study area. Collection of information on the physical geography and socio-economic distribution of the study area, including Rainfall, DEM, Slope, Land use/Land cover (LULC), Distance from the rivers, Curve number (CN), Manning coefficient, topographic wetness index (TWI), population density and Gross Domestic Product (GDP) distribution. A machine learning approach to assess flood susceptibility in the study area, using six standard statistical indices (accuracy, precision, recall, F-score, Root Mean Square Error and Kappa index) and Receiver Operating Characteristic (ROC) curves called AUG to evaluate the performance of the machine learning model. The results show that all the proposed models perform well, but performance of the MLP model is the best (AUC\u0026thinsp;=\u0026thinsp;0.951). Using the Analytic Hierarchy Process (AHP) method to determine the weights, the flood susceptibility map obtained from the MLP is overlaid with hazard and vulnerability for flood risk assessment. The results show that integrated machine learning algorithms and MCDA for flood analysis are a promising approach to flood risk assessment.\u003c/p\u003e","manuscriptTitle":"Flood risk assessment based on machine learning and multi-criteria decision analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 05:37:20","doi":"10.21203/rs.3.rs-9004660/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":"9707c395-dd1c-42ac-8f5b-83ee9dd0ba7c","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-14T06:35:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T02:58:08+00:00","index":76,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T08:50:19+00:00","index":75,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64507624,"name":"Earth and environmental sciences/Environmental sciences"},{"id":64507625,"name":"Earth and environmental sciences/Hydrology"},{"id":64507626,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2026-05-14T06:40:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 05:37:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9004660","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9004660","identity":"rs-9004660","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}