Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 27, issue 1
Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-139-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-139-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 27, issue 1
Research article
 | 
09 Jan 2023
Research article |  | 09 Jan 2023

Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models

Richard Arsenault, Jean-Luc Martel, Frédéric Brunet, François Brissette, and Juliane Mai

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Short summary
Predicting flow in rivers where no observation records are available is a daunting task. For decades, hydrological models were set up on these gauges, and their parameters were estimated based on the hydrological response of similar or nearby catchments where records exist. New developments in machine learning have now made it possible to estimate flows at ungauged locations more precisely than with hydrological models. This study confirms the performance superiority of machine learning models.
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