A Novel LSTM Approach for Reliable and Real-Time Flood Prediction in Complex Watersheds

A Novel LSTM Approach for Reliable and Real-Time Flood Prediction in Complex Watersheds | 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 A Novel LSTM Approach for Reliable and Real-Time Flood Prediction in Complex Watersheds Wassima Moutaouakil, Soufiane Hamida, Oussama ElGannour, Asmae Ouhmida, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6256506/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 In the context of global climate change, the world is increasingly experiencing abnormal phenomena, with natural disasters being among the most critical challenges. Adapting to these changes and mitigating their risks has become imperative. Floods, as one of the most devastating natural threats, are a crucial subject of study, particularly in understanding and predicting their dynamic behavior. This research highlights the importance of flood mapping and assessment using satellite imagery and advanced technologies such as Geographical Information System (GIS) and Deep Learning (DL). The study focuses on Tetouan city, located in northern Morocco, which provides ideal conditions for this research. Eleven flood conditioning factors were analyzed, including elevation, slope, aspect, Stream Power Index (SPI), Topographic Position Index (TPI), Topographic Wetness Index (TWI), curvature, drainage density (DD), distance to rivers (DR), Normalized Difference Vegetation Index (NDVI), and land use (LU). To identify the most relevant factors influencing flood occurrence, Information Gain Ratio (IGR) and Frequency Ratio (FR) methods were applied, allowing for the exclusion of non-impactful factors. The Long Short-Term Memory (LSTM) deep learning technique was utilized on a balanced dataset of 1946 samples generated through data augmentation. Additional optimization techniques were implemented to enhance the model’s performance. The findings demonstrate a high prediction accuracy of 96.06%, underscoring the model's effectiveness in flood risk assessment. Flood susceptibility prediction Deep Learning GIS remote sensing Morocco natural hazards. 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. 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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-6256506","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433496801,"identity":"81bec008-7d76-4d50-9ea7-1cf89b6dffff","order_by":0,"name":"Wassima 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