Integrating Machine Learning and Participatory GIS with Multi-Temporal Remote Sensing for Flood Susceptibility and Vulnerability Mapping in Nkhotakota, Malawi

Integrating Machine Learning and Participatory GIS with Multi-Temporal Remote Sensing for Flood Susceptibility and Vulnerability Mapping in Nkhotakota, Malawi | 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 Integrating Machine Learning and Participatory GIS with Multi-Temporal Remote Sensing for Flood Susceptibility and Vulnerability Mapping in Nkhotakota, Malawi Japhet Khendlo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8760455/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 This study presents an integrated approach for assessing flood susceptibility and vulnerability in Nkhotakota District, Malawi, by combining remote sensing, field-validated observations, and machine learning techniques. Physical flood susceptibility was estimated using the Soil Conservation Service–Curve Number (SCS-CN) method to model runoff potential, alongside Sentinel-2 imagery processed in Google Earth Engine to delineate inundation areas. Machine learning algorithms, including Support Vector Regression (SVR), Random Forest (RF), and K-Nearest Neighbour (KNN), were trained on 300 flood and non-flood points derived from ground-truth verification, with SVR providing the most accurate predictions (MAE = 0.041, MSE = 0.042, RMSE = 0.205, AUC = 0.98). Sub-watersheds were classified into high, moderate, and low susceptibility zones, highlighting flood-prone areas along major rivers and low-lying floodplains. Socio-economic vulnerability was assessed through 130 household surveys covering economic, physical–infrastructural, institutional–policy, and social–cultural dimensions. Analysis showed the highest absolute vulnerability in the economic dimension (0.48) and greatest relative sensitivity in the social dimension (rank = 2.94), reflecting the interplay of low income, agricultural dependence, and dense settlements near rivers. Combining susceptibility and vulnerability revealed hotspots where high physical exposure coincides with high social sensitivity, providing priority areas for targeted interventions. The study offers practical guidance for flood risk management, including structural measures, early warning, and community-based resilience strategies, and demonstrates the effectiveness of integrating remote sensing, ML, and participatory socio-economic data in flood risk assessments. Environmental Engineering Environmental Policy Geographic Information Systems Flood susceptibility Socio-economic vulnerability SCS-CN method Machine learning Remote sensing Nkhotakota District Full Text Additional Declarations The authors declare no competing interests. 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-8760455","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584017596,"identity":"a80d39d7-c02d-4595-aba3-7d22ad0283dd","order_by":0,"name":"Japhet Khendlo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACCQkgwVPAwMDY3gDmE6vFAKil5wCpWhgkEoh0mOTs5mMP3hjYMDDPfJ38mXeHBQN/+wHWzTx4tEjLHEs3nGOQxsA4O3eDMe8ZCQaJMwlst/FpkZPIMZPmMTgM1pLM2wZ05w0Gtts5eLXkf4NomXl2w2GQFnlCWqQlctggWmbwbmwGaTEgpEVyRpqZJNAvPIw9uZsZ57ZJ8BieSWy7/QePFokbyc8k3lTYyBm2n9384W1bnZzc8cPHbs7AowUGeAwboAxgrDYQoQEI5IlTNgpGwSgYBSMRAACeBERXWZe9EQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0008-0970-0237","institution":"Mzuzu University","correspondingAuthor":true,"prefix":"","firstName":"Japhet","middleName":"","lastName":"Khendlo","suffix":""}],"badges":[],"createdAt":"2026-02-02 04:45:54","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8760455/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8760455/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101754027,"identity":"40d3e0a5-d477-40a9-a40e-cc2354381b3e","added_by":"auto","created_at":"2026-02-03 10:41:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1103084,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptwithoutAuthors.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8760455/v1_covered_5d06531b-aa39-4a57-b7ee-c998cba66893.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIntegrating Machine Learning and Participatory GIS with Multi-Temporal Remote Sensing for Flood Susceptibility and Vulnerability Mapping in Nkhotakota, Malawi\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Mzuzu University","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 susceptibility, Socio-economic vulnerability, SCS-CN method, Machine learning, Remote sensing, Nkhotakota District","lastPublishedDoi":"10.21203/rs.3.rs-8760455/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8760455/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents an integrated approach for assessing flood susceptibility and vulnerability in Nkhotakota District, Malawi, by combining remote sensing, field-validated observations, and machine learning techniques. Physical flood susceptibility was estimated using the Soil Conservation Service\u0026ndash;Curve Number (SCS-CN) method to model runoff potential, alongside Sentinel-2 imagery processed in Google Earth Engine to delineate inundation areas. Machine learning algorithms, including Support Vector Regression (SVR), Random Forest (RF), and K-Nearest Neighbour (KNN), were trained on 300 flood and non-flood points derived from ground-truth verification, with SVR providing the most accurate predictions (MAE\u0026thinsp;=\u0026thinsp;0.041, MSE\u0026thinsp;=\u0026thinsp;0.042, RMSE\u0026thinsp;=\u0026thinsp;0.205, AUC\u0026thinsp;=\u0026thinsp;0.98). Sub-watersheds were classified into high, moderate, and low susceptibility zones, highlighting flood-prone areas along major rivers and low-lying floodplains. Socio-economic vulnerability was assessed through 130 household surveys covering economic, physical\u0026ndash;infrastructural, institutional\u0026ndash;policy, and social\u0026ndash;cultural dimensions. Analysis showed the highest absolute vulnerability in the economic dimension (0.48) and greatest relative sensitivity in the social dimension (rank\u0026thinsp;=\u0026thinsp;2.94), reflecting the interplay of low income, agricultural dependence, and dense settlements near rivers. Combining susceptibility and vulnerability revealed hotspots where high physical exposure coincides with high social sensitivity, providing priority areas for targeted interventions. The study offers practical guidance for flood risk management, including structural measures, early warning, and community-based resilience strategies, and demonstrates the effectiveness of integrating remote sensing, ML, and participatory socio-economic data in flood risk assessments.\u003c/p\u003e","manuscriptTitle":"Integrating Machine Learning and Participatory GIS with Multi-Temporal Remote Sensing for Flood Susceptibility and Vulnerability Mapping in Nkhotakota, Malawi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 03:36:51","doi":"10.21203/rs.3.rs-8760455/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":"d0d56597-c64c-4bdf-ad38-cb8791de2462","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62127139,"name":"Environmental Engineering"},{"id":62127140,"name":"Environmental Policy"},{"id":62127141,"name":"Geographic Information Systems"}],"tags":[],"updatedAt":"2026-02-03T03:36:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 03:36:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8760455","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8760455","identity":"rs-8760455","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}