Feature Attribution-Driven Flood Susceptibility Assessment: an Integrated Gradients Approach in Seti Gandaki River Basin

Feature Attribution-Driven Flood Susceptibility Assessment: an Integrated Gradients Approach in Seti Gandaki River Basin | 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 Feature Attribution-Driven Flood Susceptibility Assessment: an Integrated Gradients Approach in Seti Gandaki River Basin Biswash Kaphle, Aayush Adhikari, Aayush Kafle, Ayush Aryal, Madan Pokhrel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7052855/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract This research introduces a sophisticated framework for flood susceptibility analysis in theSeti Gandaki watershed that integrates multiple geospatial features with deep learning method-ologies. The proposed neural network model leverages ten critical geomorphological, hy-drological, and environmental features to predict flood-prone areas with high precision. Ourmethodological approach encompasses comprehensive geospatial data preprocessing, fea-ture alignment, and threshold-based balanced sampling to ensure robust model training. Thedeveloped deep learning architecture incorporates multiple hidden layers with dropout regu-larization and batch normalization, achieving exceptional performance metrics exceeding98% accuracy, 99.65% precision, and 97.22% recall. Feature attribution analysis usingIntegrated Gradients identified Topographic Wetness Index (TWI), distance to rivers, andrainfall as the primary contributors to flood susceptibility prediction. The study generatedhigh-resolution flood susceptibility maps with quantified prediction uncertainty, providingcritical spatial intelligence for flood risk management. Comparative analysis with traditionalAnalytical Hierarchy Process (AHP) revealed significant methodological differences, withonly 8.52% spatial agreement between approaches. This research demonstrates the efficacyof integrating advanced machine learning techniques with traditional geospatial analysis toenhance flood prediction capabilities, offering valuable decision support for disaster man-agement and mitigation strategies in hydrologically complex Himalayan watersheds.Keywords: Flood susceptibility, Deep learning, Geospatial analysis, Feature attribution, SetiGandaki watershed, Disaster management, Neural networks, Prediction uncertainty Flood susceptibility Deep learning Geospatial analysis Feature attribution Seti Gandaki watershed Disaster management Neural networks Prediction uncertainty Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviews received at journal 29 Aug, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviews received at journal 31 Jul, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 30 Jul, 2025 Editor invited by journal 21 Jul, 2025 Submission checks completed at journal 18 Jul, 2025 First submitted to journal 18 Jul, 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. 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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-7052855","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494691005,"identity":"61d0dc6a-8323-45fc-a360-28439a37573f","order_by":0,"name":"Biswash 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