Preprints
https://doi.org/10.5194/hess-2021-596
https://doi.org/10.5194/hess-2021-596
 
07 Jan 2022
07 Jan 2022
Status: a revised version of this preprint is currently under review for the journal HESS.

A deep learning technique-based data-driven model for accurate and rapid flood prediction

Qianqian Zhou, Shuai Teng, Xiaoting Liao, Zuxiang Situ, Junman Feng, and Gongfa Chen Qianqian Zhou et al.
  • School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, China

Abstract. An accurate and rapid urban flood prediction model is essential to support decision-making on flood management, especially under increasing extreme precipitation conditions driven by climate change and urbanization. This study developed a deep learning technique-based data-driven flood prediction model based on an integration of LSTM network and Bayesian optimization. A case study in north China was applied to test the model performance and the results clearly showed that the model can accurately predict flood maps for various hyetograph inputs, meanwhile with substantial improvements in computation time. The model predicted flood maps 19,585 times faster than the physical-based hydrodynamic model and achieved a mean relative error of 9.5 %. For retrieving the spatial patterns of water depths, the degree of similarity of the flood maps was very high. In a best case, the difference between the ground truth and model prediction was only 0.76 % and the spatial distributions of inundated paths and areas were almost identical. The proposed model showed a robust generalizability and high computational efficiency, and can potentially replace and/or complement the conventional hydrodynamic model for urban flood assessment and management, particularly in applications of real time control, optimization and emergency design and plan.

Qianqian Zhou et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-596', Anonymous Referee #1, 11 Mar 2022
    • AC1: 'Reply on RC1', Qianqian Zhou, 23 Sep 2022
  • RC2: 'Comment on hess-2021-596', Anonymous Referee #2, 02 Sep 2022
    • AC2: 'Reply on RC2', Qianqian Zhou, 23 Sep 2022

Qianqian Zhou et al.

Qianqian Zhou et al.

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Short summary
We proposed a deep learning technique-based data-driven flood prediction approach, employing an integration of the LSTM technique and Bayesian optimization approach. Results showed that the proposed model can accurately produce flood maps for various hyetograph inputs, meanwhile with substantial improvements in computation time. The proposed model can potentially replace and/or complement the conventional hydrodynamic model for urban flood assessment and management.