Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 19
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.
https://doi.org/10.5194/hess-29-4951-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 19
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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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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