Spatial Mapping and Modeling Flood Risk Susceptibility in Global Urban Floating Slums Using Remote Sensing and Machine Learning

Spatial Mapping and Modeling Flood Risk Susceptibility in Global Urban Floating Slums Using Remote Sensing and Machine Learning | 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 Spatial Mapping and Modeling Flood Risk Susceptibility in Global Urban Floating Slums Using Remote Sensing and Machine Learning KATABARWA MURENZI Gilbert, Qian Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7228512/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 Earth is urbanizing fast, and overcrowded cities are giving rise to floating slums in high-risk flood zones, an urgent yet overlooked issue. This study aims to (1) Map the spatial expansion of floating slums near submerged or flood-adjacent built-up zones, (2) Assess environmental degradation of surrounding vegetation due to slum encroachment and recurrent flooding, (3) Model flood susceptibility using hydrological and land-use/land-cover variables to identify vulnerable slum areas, and (4) Quantify built-up footprints within flood-prone zones and estimate population exposure across risk categories in informal settlements globally. This study introduces a novel integration of high-resolution multispectral satellite imagery, terrain metrics, and socio-economic indicators within a dual-stage Random Forest classification framework, enabling precise mapping of urban floating slums and flood susceptibility across five global sites with over 90% model accuracy. The results show that (1) Built-up areas expanded significantly, with Makoko increasing from 73.84% in 2019 to 82.59% in 2025; Cái Răng from 34.54–41.63%; and Manacapuru from 2.14–3.04%. (2) Vegetation cover declined, particularly in Kampong Ayer (from 65.45–60.98%), Cái Răng (49.29–42.99%), and Manacapuru (49.94–47.77%). (3) Flood susceptibility mapping revealed diverse exposure levels, with a substantial portion of each site falling into high and very high-risk zones. (4) Population exposure in very high-risk zones is severe, including 63,450 in Makoko, 388,450 in Kampong Ayer, 99,600 in Tonle Sap, 2,406,800 in Manacapuru, and 3,282,000 in Cái Răng. Urban resilience is urgently needed to limit urban floating slums related issues in line with SDG 11’s goal for inclusive and safe cities. Urban Floating Slums Flood Susceptibility Modeling Vulnerability Assessment Random Forest Google Earth Engine Full Text 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-7228512","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509816497,"identity":"b458a9b1-3131-44a1-82f2-7e57cb74b524","order_by":0,"name":"KATABARWA MURENZI Gilbert","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACAxDxwcCGhx/M5ZEAEgmEtDAzMM6oSJOTbCNFCzPPmcPGBsfgYgS0mLOfP/iBt405cfP95mMPv8hYMPCz5xgw/NyBW4tlTzKzhGQbW+K2Y2zpxjJAh0n2vDFg7D2Dx2EHkhkkDNt4gFp4zKQlgFoMbuQYMDO24dFy/jHzj8Q2icTNbVAt9gS13EhmkzhwxsDYgI3HTPIDyBYJgloem1k2VCTISRxLS5MGBjKPxJlnBQd78Tos8fHtPwb/efibDx+T/NlTJ8ffnrzxwU88WlAAM28PAw+IcYBIDQwMjD9+EK12FIyCUTAKRhAAAEbRS9UzRwcUAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0005-4789-275X","institution":"Tongji University","correspondingAuthor":true,"prefix":"","firstName":"KATABARWA","middleName":"MURENZI","lastName":"Gilbert","suffix":""},{"id":509816498,"identity":"6b071322-772a-435b-8e5e-5ab55ff5b246","order_by":1,"name":"Qian Shi","email":"","orcid":"","institution":"Tongji University School of Economics and Management","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2025-07-27 23:12:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7228512/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7228512/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94473373,"identity":"e7e08387-c22d-4e4f-8aa2-cf98b31d739d","added_by":"auto","created_at":"2025-10-27 15:44:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1258011,"visible":true,"origin":"","legend":"","description":"","filename":"ModelingFSMinGlobalUrbanFloatingSlums.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7228512/v1_covered_58b7ef66-f167-4feb-b03c-7abfe074dc35.pdf"}],"financialInterests":"","formattedTitle":"Spatial Mapping and Modeling Flood Risk Susceptibility in Global Urban Floating Slums Using Remote Sensing and Machine Learning","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":"Urban Floating Slums, Flood Susceptibility Modeling, Vulnerability Assessment, Random Forest, Google Earth Engine","lastPublishedDoi":"10.21203/rs.3.rs-7228512/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7228512/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEarth is urbanizing fast, and overcrowded cities are giving rise to floating slums in high-risk flood zones, an urgent yet overlooked issue. This study aims to (1) Map the spatial expansion of floating slums near submerged or flood-adjacent built-up zones, (2) Assess environmental degradation of surrounding vegetation due to slum encroachment and recurrent flooding, (3) Model flood susceptibility using hydrological and land-use/land-cover variables to identify vulnerable slum areas, and (4) Quantify built-up footprints within flood-prone zones and estimate population exposure across risk categories in informal settlements globally. This study introduces a novel integration of high-resolution multispectral satellite imagery, terrain metrics, and socio-economic indicators within a dual-stage Random Forest classification framework, enabling precise mapping of urban floating slums and flood susceptibility across five global sites with over 90% model accuracy. The results show that (1) Built-up areas expanded significantly, with Makoko increasing from 73.84% in 2019 to 82.59% in 2025; C\u0026aacute;i Răng from 34.54\u0026ndash;41.63%; and Manacapuru from 2.14\u0026ndash;3.04%. (2) Vegetation cover declined, particularly in Kampong Ayer (from 65.45\u0026ndash;60.98%), C\u0026aacute;i Răng (49.29\u0026ndash;42.99%), and Manacapuru (49.94\u0026ndash;47.77%). (3) Flood susceptibility mapping revealed diverse exposure levels, with a substantial portion of each site falling into high and very high-risk zones. (4) Population exposure in very high-risk zones is severe, including 63,450 in Makoko, 388,450 in Kampong Ayer, 99,600 in Tonle Sap, 2,406,800 in Manacapuru, and 3,282,000 in C\u0026aacute;i Răng. Urban resilience is urgently needed to limit urban floating slums related issues in line with SDG 11\u0026rsquo;s goal for inclusive and safe cities.\u003c/p\u003e","manuscriptTitle":"Spatial Mapping and Modeling Flood Risk Susceptibility in Global Urban Floating Slums Using Remote Sensing and Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-10 12:35:31","doi":"10.21203/rs.3.rs-7228512/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":"4fa1f0c4-8dd4-46fc-834d-0683b163f405","owner":[],"postedDate":"September 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T14:26:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-10 12:35:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7228512","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7228512","identity":"rs-7228512","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}