Assessing the adequacy of traditional hydrological models for climate change impact studies: a case for long short-term memory (LSTM) neural networks

Assessing the adequacy of traditional hydrological models for climate change impact studies: a case for long short-term memory (LSTM) neural networks

<p>Climate change impact studies are essential for understanding the effects of changing climate conditions on water resources. This paper assesses the effectiveness of long short-term memory (LSTM) neural networks compared to traditional hydrological models for these studies. Traditional hydr...

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Main Authors: J.-L. Martel, F. Brissette, R. Arsenault, R. Turcotte, M. Castañeda-Gonzalez, W. Armstrong, E. Mailhot, J. Pelletier-Dumont, G. Rondeau-Genesse, L.-P. Caron
Format: Article
Language:English
Published: Copernicus Publications 2025-07-01
Series:Hydrology and Earth System Sciences
Online Access:https://hess.copernicus.org/articles/29/2811/2025/hess-29-2811-2025.pdf
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