Resolving the accuracy-scale-cost trilemma: Bridging inequitable access to flood information with AI generated flood maps

Resolving the accuracy-scale-cost trilemma: Bridging inequitable access to flood information with AI generated flood maps | 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 Social Sciences - Article Resolving the accuracy-scale-cost trilemma: Bridging inequitable access to flood information with AI generated flood maps Rudi Stouffs, Abraham Wu, Ye Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6460834/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The mapping of flood-prone areas is crucial for disaster response and adaptation. With a flood map, communities, insurers, and planners can prepare and be insured against risk damages. However, hindered by high resource demand, open-access flood maps, such as the FEMA National Flood Hazard Layer (NFHL) in the US, suffer from incomplete information. Currently, only one-third of all river channels in the country is complete, leaving population and structures in the other two-thirds vulnerable. To address this data gap, we present a methodology for completing the missing flood zones in the FEMA NFHL based on generative AI. The model learns patterns from the mapped areas and generates flood maps at 30 meters resolution in the unmapped areas with up to 81% and 65% in national scale studies. With the new flood data covering all river channels, we found that there are 11 million exposed population, and 4.1 million buildings located in flood zones that are unmapped by the NFHL as of 2023. This quantifies the inequitable access to open flood information across the country. Additionally, we found that 40% of all urban areas are inadequately mapped by the NFHL, with 103 out of the total 917 Core-based statistical areas having little to no FEMA maps even though there are at least more than 1% of the population at risk. More alarmingly, demographic analyses have revealed that these cities also have the highest percentage of elderly and young populations, who are more vulnerable to crises. This study highlights the use of generative AIs in scaling up mapping processes. With the completed NFHL flood map, exposed communities that were previously left out of the NFHL can begin planning for flood resilience today. Earth and environmental sciences/Environmental social sciences/Environmental impact Earth and environmental sciences/Hydrology Scientific community and society/Social sciences/Climate change/Attribution Earth and environmental sciences/Environmental social sciences/Climate-change adaptation Generative AI Disaster Management Resilience Planning Policy Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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. 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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-6460834","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Social Sciences - Article","associatedPublications":[],"authors":[{"id":444947519,"identity":"2355d652-16d1-445c-a051-bb31a1366e7c","order_by":0,"name":"Rudi Stouffs","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYLCCChsGOQiLjRjlIEVn0hiMSdeS2EC0Fvn5zcckDiTUpW+XSH/A8KHsMIN8/wL8WgyOsaUBtRzO3Tkjx4BxxrnDDAY3HhDQwsZjJv3xx4HcDTdyGJh524BaJA4QcFgbjxnYYQY30h8w/wVqkZ9BQAvDMbAW5gSDGwkGzIxALQznGwj5JS3ZAugXww1n3hgc7DmXzmNwg4Al8s2HD94AOkze4Hj6wwc/yqzl5PsJOQwZgNTyMEgkkKAFAvhJsWUUjIJRMApGAgAAqs9HJpFUZlEAAAAASUVORK5CYII=","orcid":"","institution":"National University of Singapore","correspondingAuthor":true,"prefix":"","firstName":"Rudi","middleName":"","lastName":"Stouffs","suffix":""},{"id":444947520,"identity":"2eb8a2e1-9c6d-4103-ab0c-f7a6818e4ca2","order_by":1,"name":"Abraham Wu","email":"","orcid":"https://orcid.org/0000-0001-9586-3201","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Abraham","middleName":"","lastName":"Wu","suffix":""},{"id":444947521,"identity":"8497b5b9-6082-40b8-9d91-a559d27bf370","order_by":2,"name":"Ye Zhang","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-04-16 07:51:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6460834/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6460834/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82127646,"identity":"d307076d-59f1-4d08-acc0-54d30c8d284a","added_by":"auto","created_at":"2025-05-07 04:33:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5438676,"visible":true,"origin":"","legend":"Article File","description":"","filename":"ManuscriptwithNames.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6460834/v1_covered_f7238716-2871-4a29-a3d7-93f0b976bbab.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Resolving the accuracy-scale-cost trilemma: Bridging inequitable access to flood information with AI generated flood maps","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Generative AI, Disaster Management, Resilience Planning, Policy","lastPublishedDoi":"10.21203/rs.3.rs-6460834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6460834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The mapping of flood-prone areas is crucial for disaster response and adaptation. 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Additionally, we found that 40% of all urban areas are inadequately mapped by the NFHL, with 103 out of the total 917 Core-based statistical areas having little to no FEMA maps even though there are at least more than 1% of the population at risk. More alarmingly, demographic analyses have revealed that these cities also have the highest percentage of elderly and young populations, who are more vulnerable to crises. This study highlights the use of generative AIs in scaling up mapping processes. 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