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.
Research article
| 
04 Nov 2025
Research article |
| 04 Nov 2025
| 04 Nov 2025
Deep learning of flood forecasting by considering interpretability and physical constraints
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Cited articles
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Beaudoing, H. and Rodell, M.: GLDAS Noah Land Surface Model L4 3 hourly 0.25×0.25 degree V2.1, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/E7TYRXPJKWOQ, 2020.
Birkholz, S., Muro, M., Jeffrey, P., and Smith, H. M.: Rethinking the relationship between flood risk perception and flood management, Sci. Total Environ., 478, 12–20, https://doi.org/10.1016/j.scitotenv.2014.01.061, 2014.
Brauwers, G. and Frasincar, F.: A General Survey on Attention Mechanisms in Deep Learning, IEEE Trans. Knowl. Data Eng., 35, 3279–3298, https://doi.org/10.1109/TKDE.2021.3126456, 2023.
Cao, Q., Zhang, H., Zhu, F., Hao, Z., and Yuan, F.: Multi-step-ahead flood forecasting using an improved BiLSTM-S2S model, J. Flood Risk Manag., 15, e128274, https://doi.org/10.1111/jfr3.12827, 2022.
