Proceedings of IAHS
Proceedings of IAHS
PIAHS
Articles | Volume 386
Articles | Volume 386
https://doi.org/10.5194/piahs-386-141-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/piahs-386-141-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 386
Post-conference publication
 | 
19 Apr 2024
Post-conference publication |  | 19 Apr 2024

Dual-Stage Attention-Based LSTM Network for Multiple Time Steps Flood Forecasting

Fan Wang, Weiqi Wang, Wuxia Bi, Wenqing Lin, and Dawei Zhang

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Short summary
Flood forecasting is important for controlling floods, and lately people have been paying more attention to using data-driven models instead of just physical or conceptual ones. This study uses an state of art neural networks to make the most out of all kinds of historical data and precipitation forecasts for predicting floods over different time scales. We tested it in serval watersheds in China, and it turned out to be really accurate and reliable.
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