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.
Research article
| 
19 Dec 2025
Research article |
| 19 Dec 2025
| 19 Dec 2025
An explainable deep learning model based on hydrological principles for flood simulation and forecasting
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Cited articles
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Bindas, T., Tsai, W., Liu, J., Rahmani, F., Feng, D., Bian, Y., Lawson, K., and Shen, C.: Improving river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning, Water Resour. Res., 60, e2023WR035337, https://doi.org/10.1029/2023WR035337, 2024.
Boucher, M. A., Quilty, J., and Adamowski, J.: Data assimilation for streamflow forecasting using extreme learning machines and multilayer perceptrons, Water Resour. Res., 56, e2019WR026226, https://doi.org/10.1029/2019WR026226, 2020.
Chen, C., Jiang, J., Liao, Z., Zhou, Y., Wang, H., and Pei, Q.: A short-term flood prediction based on spatial deep learning network: A case study for Xi County, China, J. Hydrol., 607, 127535, https://doi.org/10.1016/j.jhydrol.2022.127535, 2022.
Cheng, C., Zhao, M., Chau, K. W., and Wu, X.: Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure, J. Hydrol., 316, 129–140, https://doi.org/10.1016/j.jhydrol.2005.04.022, 2006.
