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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2134', Emilio Graciliano Ferreira Mercuri, 21 Sep 2024
    • AC1: 'Reply on RC1', Jean-Luc Martel, 07 Nov 2024
  • RC2: 'Comment on egusphere-2024-2134', Andre Ballarin, 26 Dec 2024
    • AC2: 'Reply on RC2', Jean-Luc Martel, 24 Jan 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (27 Feb 2025) by Zhongbo Yu
AR by Jean-Luc Martel on behalf of the Authors (11 Apr 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (22 Apr 2025) by Zhongbo Yu
RR by Andre Ballarin (23 Apr 2025)
ED: Publish subject to minor revisions (review by editor) (17 Jun 2025) by Zhongbo Yu
AR by Jean-Luc Martel on behalf of the Authors (27 Jun 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (30 Jun 2025) by Zhongbo Yu
AR by Jean-Luc Martel on behalf of the Authors (30 Jun 2025)
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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.
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