Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-4951-2025
https://doi.org/10.5194/hess-29-4951-2025
Research article
 | 
06 Oct 2025
Research article |  | 06 Oct 2025

Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis

Jean-Luc Martel, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, François Brissette, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Simon Lachance-Cloutier, Gabriel Rondeau-Genesse, and Louis-Philippe Caron

Viewed

Total article views: 1,914 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,431 396 87 1,914 15 40 59
  • HTML: 1,431
  • PDF: 396
  • XML: 87
  • Total: 1,914
  • Supplement: 15
  • BibTeX: 40
  • EndNote: 59
Views and downloads (calculated since 20 Aug 2024)
Cumulative views and downloads (calculated since 20 Aug 2024)

Viewed (geographical distribution)

Total article views: 1,914 (including HTML, PDF, and XML) Thereof 1,914 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Oct 2025
Download
Short summary
This study explores six methods to improve the ability of long short-term memory (LSTM) neural networks to predict peak streamflows, crucial for flood analysis. By enhancing data inputs and model techniques, the research shows that LSTM models can match or surpass traditional hydrological models in simulating peak flows. Tested on 88 catchments in Quebec, Canada, these methods offer promising strategies for better flood prediction.
Share