Advanced Flood Risk Assessment Using a Novel Hybrid Transformer-SE-ANN Framework

Advanced Flood Risk Assessment Using a Novel Hybrid Transformer-SE-ANN Framework | 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 Advanced Flood Risk Assessment Using a Novel Hybrid Transformer-SE-ANN Framework Muhammad Own Raza, Muhammad Hanzla, Mahnoor Mukhtiar, Nadeem shaukat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7103826/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Flood prediction is a vital yet complex task in disaster management, necessitating precise modeling of multifaceted factors such as weather patterns, topography, and human influences. In this study, we introduce a novel hybrid approach, the Hybrid Transformer SE-ANN, designed to improve flood probability forecasting by combining the Transformer architecture, Squeeze-and-Excitation (SE) attention mechanisms, and Artificial Neural Networks (ANN). The Transformer captures long range dependencies in the data, the SE module emphasizes critical features, and the ANN excels at discerning intricate patterns. Our model demonstrated exceptional performance, achieving a Mean Absolute Error (MAE) of 0.00234, Root Mean Squared Error (RMSE) of 0.00297, Mean Absolute Percentage Error (MAPE) of 0.50%, and an R 2 score of 0.99599, reflecting its remarkable accuracy and precision. To ensure these results are robust and free from bias, overfitting, or data leakage, we applied rigorous validation techniques, including SHAP and LIME for interpretability, alongside actual vs. predicted plots and Kernel Density Estimation (KDE) curves to confirm consistency and generalizability. The Hybrid Transformer-SE-ANN offers a reliable and highly effective tool for flood prediction, with promising implications for real-world deployment. Artificial Intelligence and Machine Learning Environmental Engineering Marine and Freshwater Ecology flood prediction hybrid deep learning Transformer Squeeze-and-Excitation artificial neural network Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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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In this study, we introduce a novel hybrid approach, the Hybrid Transformer SE-ANN, designed to improve flood probability forecasting by combining the Transformer architecture, Squeeze-and-Excitation (SE) attention mechanisms, and Artificial Neural Networks (ANN). The Transformer captures long range dependencies in the data, the SE module emphasizes critical features, and the ANN excels at discerning intricate patterns. Our model demonstrated exceptional performance, achieving a Mean Absolute Error (MAE) of 0.00234, Root Mean Squared Error (RMSE) of 0.00297, Mean Absolute Percentage Error (MAPE) of 0.50%, and an\u003cem\u003e R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e score of 0.99599, reflecting its remarkable accuracy and precision. 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