Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 6
Articles | Volume 30, issue 6
https://doi.org/10.5194/hess-30-1625-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-1625-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 6
Research article
 | 
30 Mar 2026
Research article |  | 30 Mar 2026

A highly generalizable data-driven model for spatiotemporal urban flood dynamics real-time forecasting based on coupled CNN and ConvLSTM

Wangqi Lou, Xichao Gao, Joseph Hun Wei Lee, Jiahong Liu, Lirong Dong, and Kai Gao

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Latest update: 08 Jun 2026
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Short summary
With global warming and urbanization accelerating, urban flooding is becoming more severe. Real-time forecasting plays a key role in disaster mitigation, but traditional hydrodynamic models are too resource-intensive for timely prediction. Machine learning models offer high efficiency but often lack accuracy in simulating spatiotemporal flood dynamics. This study proposes a new data-driven model, which performs well in a flood-prone area of Macao.
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