Articles | Volume 27, issue 9
https://doi.org/10.5194/hess-27-1791-2023
© Author(s) 2023. 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-27-1791-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 May 2023
Research article |
| 05 May 2023
| 05 May 2023
A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions
Qianqian Zhou
CORRESPONDING AUTHOR
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Shuai Teng
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Zuxiang Situ
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Xiaoting Liao
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Junman Feng
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Gongfa Chen
CORRESPONDING AUTHOR
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Jianliang Zhang
Guangdong Communication Planning and Design Institute Group Co., Ltd, Guangzhou 510507, China
Zonglei Lu
GRUNDFOS Pumps (Shanghai) Co., Ltd. Guangzhou Branch, Guangzhou 510095, China
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- Novel data-driven model for flood prediction using big data: Social resilience and risk management R. Srivastava et al. https://doi.org/10.31893/multiscience.2025ss0315
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- Rapid urban flood inundation forecasting using a physics-informed deep learning approach F. Yang et al. https://doi.org/10.1016/j.jhydrol.2024.131998
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- Integrating net rainfall calculation in deep learning-based surrogate modeling frameworks for 2D flood prediction J. Farfán-Durán et al. https://doi.org/10.1016/j.jhydrol.2025.133632
- Spatial Assessment of Urban Flood Resilience Using a GESIS-ML Framework: A Case Study of Chongqing, China Y. Li et al. https://doi.org/10.3390/su18041988
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- Enhancing multi-step-ahead prediction of wave propagation with the CAE-LSTM model: a novel deep learning-based approach to flood dynamics Z. Han et al. https://doi.org/10.1080/19475705.2025.2588708
- Designing an inundation monitoring and real-time urban flood forecasting system: a synthetic study V. Tran et al. https://doi.org/10.1016/j.jhydrol.2026.135520
Saved (final revised paper)
Latest update: 30 May 2026
Short summary
A deep-learning-based data-driven model for flood predictions in temporal and spatial dimensions, with the integration of a long short-term memory network, Bayesian optimization, and transfer learning is proposed. The model accurately predicts water depths and flood time series/dynamics for hyetograph inputs, with substantial improvements in computational time. With transfer learning, the model was well applied to a new case study and showed robust compatibility and generalization ability.
A deep-learning-based data-driven model for flood predictions in temporal and spatial...
