Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 21
Articles | Volume 29, issue 21
https://doi.org/10.5194/hess-29-5955-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-5955-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 21
Research article
 | 
04 Nov 2025
Research article |  | 04 Nov 2025

Deep learning of flood forecasting by considering interpretability and physical constraints

Ting Zhang, Ran Zhang, Jianzhu Li, and Ping Feng

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Short summary
This study presents a model integrating attention mechanisms and physical constraints to improve flood prediction. It forecasts floods up to 6 h in advance. The model enhances accuracy by focusing on critical input features and historical patterns. Results demonstrate its superior performance compared to other models, offering improved flood prediction with greater interpretability and alignment with physical laws.
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