Articles | Volume 30, issue 11
https://doi.org/10.5194/hess-30-3623-2026
© Author(s) 2026. 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-30-3623-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2026
Research article |
| 15 Jun 2026
| 15 Jun 2026
Which strategy to improve the performances of an LSTM-based model for extreme stream temperature values?
Mohamed Saadi
CORRESPONDING AUTHOR
Institut de Mécanique des Fluides de Toulouse, Université de Toulouse, Toulouse INP, CNRS, 31400 Toulouse, France
Louis Guichard
Institut de Mécanique des Fluides de Toulouse, Université de Toulouse, Toulouse INP, CNRS, 31400 Toulouse, France
now at: UMR 5600 Environnement Ville Société (EVS), 69372 Lyon CEDEX 08, France
Gabrielle Cognot
Institut de Mécanique des Fluides de Toulouse, Université de Toulouse, Toulouse INP, CNRS, 31400 Toulouse, France
Laurent Labbouz
Eaucea, 31000 Toulouse, France
Hélène Roux
Institut de Mécanique des Fluides de Toulouse, Université de Toulouse, Toulouse INP, CNRS, 31400 Toulouse, France
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Short summary
LSTM (Long Short-Term Memory) networks are excellent deep-learning tools to reproduce stream temperature observations, but their performances over the range of extreme (summer) stream temperature values have been overlooked. We tested strategies to improve the LSTM performances over the highest 10 % values of stream temperature observations. We found that the best strategy is to train the LSTM models at several locations with input variables that include static, catchment and reach attributes.
LSTM (Long Short-Term Memory) networks are excellent deep-learning tools to reproduce stream...
