FloodTransformer: Efficient real-time high-resolution flood forecasting
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Date
2026
Authors
Gu, Z.
Kang, J.
Jin, W.
Tong, F.
Guo, J.Y.
Jia, W.
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Environmental Modelling & Software, 2026; 197:106832-1-106832-17
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Zhanzhong Gu, Jiachen Kang, Wenzheng Jin, Feifei Tong, Jay Guo, Wenjing Jia
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Abstract
Flood forecasting is crucial for disaster planning and risk management, yet conventional hydrodynamicbased approaches are often slow in response and computationally intensive. We present a hybrid framework leveraging traditional hydrodynamic modelling with a novel AI model to enable accurate, real-time, and highresolution flood prediction. To address the computational challenges of large-scale, dense flood prediction, we develop an efficient flood prediction model, FloodTransformer, which possesses three key novelties: variablesize cell embedding, tokenised time-sequence encoding, and physics-informed multi-task optimisation. These components effectively capture complex spatiotemporal dependencies, allowing accurate sequential predictions in a single run. Comprehensive evaluations on both simulated and historical flood events demonstrate FloodTransformer’s excellent accuracy and efficiency: NSE 0.9445, KGE 0.9759 for water-depth prediction, and IoU 0.8180, F1 0.8997 for inundation classification, outperforming all comparative models. With 3s inference enabling multiple horizons in one pass, FloodTransformer offers a robust and practical solution for operational flood risk management.
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© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/ 4.0/).