Adaptive Residual-Informed Deep Transfer Learning for Accurate Flood Depth Estimation Using Spatiotemporal Feature Learning

Adaptive Residual-Informed Deep Transfer Learning for Accurate Flood Depth Estimation Using Spatiotemporal Feature 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 Adaptive Residual-Informed Deep Transfer Learning for Accurate Flood Depth Estimation Using Spatiotemporal Feature Learning Vasumathi G, R. Vani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8713985/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Prediction of the depth of floods is crucial in minimizing the devastating impact of floods on human settlement, structures and environment based on timely risk assessment and competent management of the calamity. Despite all these advances, many models face issues in an accurate pixel based flood depth estimation, particularly in the urban and coastal complexes and there is need to develop a predictive model that is more robust and efficient. To address them, this paper proposes a new hybrid framework that integrates two more recent approaches: SPYRA model of spatiotemporal residual feature extraction and MIRAI network of MoCo-informed residual adaptive inference. The SPYRA model indicates the hierarchical and spatial-temporal pattern of flood images and the MIRAI network improves the situation by the use of adaptive learning and contrastive transfer learning, which enables predicting the depth of floods to be valid and explainable. The suggested work has been unique because it integrates the residual features extraction and adaptive inference synergistically hence enhancing the generalization and reducing the prediction error and efficiency of the computational techniques. The experiment results demonstrate the usefulness of the proposed framework over nine additional deep learning and machine learning frameworks. MIRAI model has the highest accuracy of 99%, lowest RMSE of 0.13, MAE of 0.09, highest R2 of 0.94 and lowest MAPE of 3.2% which is the power and effectiveness of the model. The additional tests including a feature distribution, error distributions, and aligning the actual and the predicted depth of the floods also confirm the validity and the interpretability of the proposed method. Flood prediction Deep Learning Image Processing Transfer Learning Disaster Management Environment Systems and Early Warning Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor invited by journal 03 Feb, 2026 Editor assigned by journal 27 Jan, 2026 First submitted to journal 27 Jan, 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. 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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-8713985","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585917450,"identity":"5bb86394-3bea-44ce-9406-f24487c1d3e3","order_by":0,"name":"Vasumathi G","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie2QsQrCMBRFXyl0qnSNFP8hIuhS9FcMBV0dOwqV5+YoLfgJDv2EJxlciq5CF0Fwjri4aaSLU+oomDOEG7gHLg/AYvlJ3N1Z8UgHZw71WwcDXtzNkkldpu8Uvx/6pawzQXMf+HHL2zkeR4O1TO8Kok5BLp6Nyuk64zesxKYSyAgmvYKcJTcp7UwWIsdqzEKBepgUWkFmVojLFh5GWkkVwbNZCYJFN/VLcrJQzPUw+kJhXuxkSSy0gqzkcS+XDYoXyP1D8aEeNr2oJBl2Vvvl1agAG3/+3qdyjf33MmpqWCwWy9/zAnNwUyYJeV1OAAAAAElFTkSuQmCC","orcid":"","institution":"SRM Institute of Science and Technology: SRM Institute of Science and Technology (Deemed to be University)","correspondingAuthor":true,"prefix":"","firstName":"Vasumathi","middleName":"","lastName":"G","suffix":""},{"id":585917451,"identity":"ade97d0b-45b5-4c88-8c8a-f43d87e334aa","order_by":1,"name":"R. 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