Articles | Volume 27, issue 9
https://doi.org/10.5194/hess-27-1791-2023
https://doi.org/10.5194/hess-27-1791-2023
Research article
 | 
05 May 2023
Research article |  | 05 May 2023

A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions

Qianqian Zhou, Shuai Teng, Zuxiang Situ, Xiaoting Liao, Junman Feng, Gongfa Chen, Jianliang Zhang, and Zonglei Lu

Viewed

Total article views: 3,875 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,744 1,047 84 3,875 100 60 58
  • HTML: 2,744
  • PDF: 1,047
  • XML: 84
  • Total: 3,875
  • Supplement: 100
  • BibTeX: 60
  • EndNote: 58
Views and downloads (calculated since 07 Jan 2022)
Cumulative views and downloads (calculated since 07 Jan 2022)

Viewed (geographical distribution)

Total article views: 3,875 (including HTML, PDF, and XML) Thereof 3,724 with geography defined and 151 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 04 Nov 2024
Download
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.