Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 24
Articles | Volume 29, issue 24
https://doi.org/10.5194/hess-29-7217-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-7217-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 24
Research article
 | 
19 Dec 2025
Research article |  | 19 Dec 2025

An explainable deep learning model based on hydrological principles for flood simulation and forecasting

Xin Xiang, Shenglian Guo, Chenglong Li, and Yun Wang

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Short summary
Deep learning models achieve strong results in hydrological simulations but often lack links to physical processes. We integrate the principles of the Xinanjiang rainfall–runoff model into a recurrent neural network layer, then combine it with long short-term memory layers. This design improves accuracy while keeping the model explainable, showing small errors in flood peaks and timing.
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