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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Mar 2026
Research article |
| 30 Mar 2026
| 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
State Key Laboratory of Water Cycle and Water Security, Beijing, 100038, Beijing, China
China Institute of Water Resources and Hydropower Research, Yuyuantan South Road, Haidian District, Beijing, 100038, Beijing, China
Xichao Gao
CORRESPONDING AUTHOR
State Key Laboratory of Water Cycle and Water Security, Beijing, 100038, Beijing, China
China Institute of Water Resources and Hydropower Research, Yuyuantan South Road, Haidian District, Beijing, 100038, Beijing, China
Joseph Hun Wei Lee
Macau University of Science and Technology, Avenida WaiLong, Taipa, Macau, 999078, Macau, China
Jiahong Liu
State Key Laboratory of Water Cycle and Water Security, Beijing, 100038, Beijing, China
China Institute of Water Resources and Hydropower Research, Yuyuantan South Road, Haidian District, Beijing, 100038, Beijing, China
Lirong Dong
Macau University of Science and Technology, Avenida WaiLong, Taipa, Macau, 999078, Macau, China
Kai Gao
State Key Laboratory of Water Cycle and Water Security, Beijing, 100038, Beijing, China
China Institute of Water Resources and Hydropower Research, Yuyuantan South Road, Haidian District, Beijing, 100038, Beijing, China
Related authors
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Xichao Gao, Zhiyong Yang, Dawei Han, Kai Gao, and Qian Zhu
Hydrol. Earth Syst. Sci., 25, 6023–6039, https://doi.org/10.5194/hess-25-6023-2021, https://doi.org/10.5194/hess-25-6023-2021, 2021
Short summary
Short summary
We proposed a theoretical framework and conducted a laboratory experiment to understand the relationship between wind and the rainfall–runoff process in urban high-rise building areas. The runoff coefficient (relating the amount of runoff to the amount of precipitation received) found in the theoretical framework was close to that found in the laboratory experiment.
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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.
With global warming and urbanization accelerating, urban flooding is becoming more severe....
