Integrated Flood Hazard and Economic Risk Assessment Using Satellite Data and Hybrid Modeling

Integrated Flood Hazard and Economic Risk Assessment Using Satellite Data and Hybrid Modeling | 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 Integrated Flood Hazard and Economic Risk Assessment Using Satellite Data and Hybrid Modeling Mohammad Roohi, Hamid Reza Ghafouri, Seyed Mohammad Ashrafi, Mahdi Motagh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8772268/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Floods represent one of the most destructive natural hazards, inflicting substantial environmental degradation, economic losses, and human casualties worldwide. This study presents an integrated framework combining deep learning, remote sensing and hydraulic modelling to estimate flood depth, hazard and economic losses in the Dez River Basin (southwestern Iran). A U-Net convolutional network was trained on Sentinel-2 and Landsat 8/9 imagery to delineate inundation extents for six major flood events (2016–2023). Flood depths were estimated using the Floodwater Depth Estimation Tool (FwDET) applied to the U-Net inundation maps and 10-m TanDEM-X DEM. Flow velocities were obtained from HEC-RAS simulations driven by observed discharge records. Depth and velocity maps were combined with a debris-flow coefficient to compute pixel-wise Hazard Risk (HR) indices and classify areas into four risk levels. Economic damages were estimated by overlaying HR maps with land-use data and applying depth-dependent monetary coefficients. The U-Net model achieved mean IoU values of 70–71.3% and accuracy above 95%, while the integrated approach produced spatially consistent HR maps highlighting urban concentrations of high risk. Results show that urban losses were substantially greater than agricultural losses for the examined events. The workflow demonstrates an operationally feasible, data-efficient pathway to deliver flood hazard products and monetary loss estimates that can support emergency response and planning. The methodology is adaptable to other basins and can be extended using higher-resolution imagery or additional in-situ measurements to improve accuracy and operational readiness. Deep learning FwDET U-Net HEC-RAS Flood hazard risk maps Full Text Supplementary Files highlight.pdf Supplementaryfile.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 07 Feb, 2026 Editor assigned by journal 05 Feb, 2026 First submitted to journal 03 Feb, 2026 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-8772268","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587509485,"identity":"9a846674-004f-4727-bcb5-92dc80838905","order_by":0,"name":"Mohammad 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