Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-4951-2025
© Author(s) 2025. 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-29-4951-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2025
Research article |
| 06 Oct 2025
| 06 Oct 2025
Exploring the ability of LSTM-based hydrological models to simulate streamflow time series for flood frequency analysis
Jean-Luc Martel
CORRESPONDING AUTHOR
Hydrology, Climate and Climate Change (HC3) laboratory, École de technologie supérieure, Montréal, H3C 1K3, Canada
Richard Arsenault
Hydrology, Climate and Climate Change (HC3) laboratory, École de technologie supérieure, Montréal, H3C 1K3, Canada
Richard Turcotte
Direction principale de l'expertise hydrique (DPEH), Ministère de l'Environnement et de la Lutte contre les changements climatiques, de la Faune et des Parcs (MELCCFP), Québec, G1R 5V7, Canada
Mariana Castañeda-Gonzalez
Hydrology, Climate and Climate Change (HC3) laboratory, École de technologie supérieure, Montréal, H3C 1K3, Canada
François Brissette
Hydrology, Climate and Climate Change (HC3) laboratory, École de technologie supérieure, Montréal, H3C 1K3, Canada
William Armstrong
Hydrology, Climate and Climate Change (HC3) laboratory, École de technologie supérieure, Montréal, H3C 1K3, Canada
Edouard Mailhot
Direction principale de l'expertise hydrique (DPEH), Ministère de l'Environnement et de la Lutte contre les changements climatiques, de la Faune et des Parcs (MELCCFP), Québec, G1R 5V7, Canada
Jasmine Pelletier-Dumont
Direction principale de l'expertise hydrique (DPEH), Ministère de l'Environnement et de la Lutte contre les changements climatiques, de la Faune et des Parcs (MELCCFP), Québec, G1R 5V7, Canada
Simon Lachance-Cloutier
Direction principale de l'expertise hydrique (DPEH), Ministère de l'Environnement et de la Lutte contre les changements climatiques, de la Faune et des Parcs (MELCCFP), Québec, G1R 5V7, Canada
Gabriel Rondeau-Genesse
Ouranos, Montréal, H3A 1B9, Canada
Louis-Philippe Caron
Ouranos, Montréal, H3A 1B9, Canada
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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.
This study explores six methods to improve the ability of long short-term memory (LSTM) neural...
