Region-specific assessment of flood disaster risk and contributing factors, based on historical data and machine learning

Region-specific assessment of flood disaster risk and contributing factors, based on historical data 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 Region-specific assessment of flood disaster risk and contributing factors, based on historical data and machine learning Yu Yang, Wen Zhu, Qiuan Zhu, Jiaxin Jin, Shanhu Jiang, Shanshui Yuan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6242832/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jan, 2026 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract This study assessed risk of major flooding across the globe based on data in the Emergency Events Database spanning 1980 to 2023 and two machine learning methods, extreme gradient boosting and random forest. A flood disaster index was calculated for politically defined provinces around the world using a combination of analytic hierarchy processing and entropy weighting. The resulting indices, together with hydro-meteorological, topographic, vegetation and economic variables, were used to train two machine learning algorithms, which ranked 20 variables according to their relative contribution to flood risk in areas differing in climate zones or levels of socio-economic development. The two algorithms did not substantially differ from each other in their rankings. The modeling suggests that low and middle latitudes are at greater risk of flooding than high latitudes, and it identified the following areas as particularly vulnerable: China, South Asia, western Arabian Peninsula, western Germany, Java (Indonesia), Zulia (Venezuela), and eastern Australia. Around the world, risk of flooding depends much more on river network density than on surface runoff. Other major determinants of major flood risk depend on the climate zone: in the tropics, economy and precipitation are major determinants; in arid regions, vegetation cover; in temperate regions, population and prolonged heavy rainfall; in cold regions, precipitation and surface soil moisture; and in polar regions, topographic factors. In the socio-economically defined "Global North", precipitation may be the primary determinant, while in the "Global South", economic factors may be more crucial. Hazard metric Influencing factor Machine learning Risk assessment Full Text Cite Share Download PDF Status: Published Journal Publication published 21 Jan, 2026 Read the published version in Natural Hazards → Version 1 posted Editorial decision: Major revisions 13 Aug, 2025 Reviewers agreed at journal 04 Apr, 2025 Reviewers invited by journal 17 Mar, 2025 Editor assigned by journal 17 Mar, 2025 First submitted to journal 17 Mar, 2025 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-6242832","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430200783,"identity":"3095d2c5-84f8-4cd1-916e-b355d30af868","order_by":0,"name":"Yu 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