Inundation Hotspot: An insurance data and machine learning based fine-scale urban flood inundation map of China

Inundation Hotspot: An insurance data and machine learning based fine-scale urban flood inundation map of China | 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 Inundation Hotspot: An insurance data and machine learning based fine-scale urban flood inundation map of China Kaihao Long, Jiahui Kang, Jiawen Li, Qun Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2378262/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 Urban flood inundation usually causes unpredictable losses in cities, and thus establishing corresponding P&C insurance is of great importance to society. However, due to lack of related data, traditional hydrodynamic model is impossible to be applied to all regions for risk simulation and assessment. To solve this issue, based on the historical insurance claims caused by typhoon and rainstorm before 2021 in China, and combining with the weather data, digital elevation model (DEM), and building outline data, we generated an inundation risk map with adaptive clustering algorithm, and the accuracy of this map is validated by the official flood inundation spots. The urban flood inundation risk map has been embedded into the primary insurance workflow, which can benefit risk management, aid in quantifying risk exposures and improve risk prevention and relief strategies. During application, many assets with high flood risks are identified prior to underwriting, and therefore the losses due to floods are largely reduced. In summary, this study shows the possibility of building an efficient urban flood inundation risk map based on big data and machine learning method, which is more applicable than pure hydrology model in the scenario of insurance, indicating a new direction for flood risk assessment. Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Urban flood inundation risk map insurance DEM data machine learning 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-2378262","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":487968161,"identity":"6f9973d8-1a6e-4b11-9a94-1adf518043ed","order_by":0,"name":"Kaihao Long","email":"","orcid":"","institution":"Ping An Property \u0026 Casualty Insurance Company of China, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Kaihao","middleName":"","lastName":"Long","suffix":""},{"id":487968162,"identity":"ea6d055b-c2de-4e71-bd12-c88df3b05bb5","order_by":1,"name":"Jiahui Kang","email":"","orcid":"","institution":"Ping An Property \u0026 Casualty Insurance Company of China, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Kang","suffix":""},{"id":487968163,"identity":"92635d99-8bd5-41f5-bc97-dc1823db4ef3","order_by":2,"name":"Jiawen Li","email":"","orcid":"","institution":"Ping An Property \u0026 Casualty Insurance Company of China, Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jiawen","middleName":"","lastName":"Li","suffix":""},{"id":487968164,"identity":"377d28af-a8ba-4c8b-98da-3684c72110a9","order_by":3,"name":"Qun Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACPmYYi70BSBhYENbCBtfCcwCkRYIILXCWRAKYJEILO4/hbd4dhxPnz3x+dcOPAgkG/vbuBAIO4zG25j2Tltg4O6fsZg/QYRJnzm4gpMVMmrfNJrFZOiftBg9Qi4FELlFaJBLbJM+k3fxDghabxB4J9mO3ibSFrdhyblua8QyeHLbbMgYSPAT9ws9/eOONt22HZee3H392880fGzn+9l78WkAAFBeODQw8BiAOD0HlMC32wBTzgCjVo2AUjIJRMPIAAKybPKPHSnF8AAAAAElFTkSuQmCC","orcid":"","institution":"Ping An Property \u0026 Casualty Insurance Company of China, Ltd","correspondingAuthor":true,"prefix":"","firstName":"Qun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2022-12-14 14:14:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2378262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2378262/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87242767,"identity":"24cd12ff-286c-4d49-9b0e-650ff25e75f2","added_by":"auto","created_at":"2025-07-22 01:51:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":951462,"visible":true,"origin":"","legend":"","description":"","filename":"Inundationhotspotmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2378262/v1_covered_5729e34a-d1cf-4148-8566-8043738751a2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inundation Hotspot: An insurance data and machine learning based fine-scale urban flood inundation map of China","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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 flood inundation, risk map, insurance, DEM data, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-2378262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2378262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Urban flood inundation usually causes unpredictable losses in cities, and thus establishing corresponding P\u0026C insurance is of great importance to society. 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