Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 22
Articles | Volume 26, issue 22
https://doi.org/10.5194/hess-26-5793-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5793-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 22
Research article
 | 
17 Nov 2022
Research article |  | 17 Nov 2022

How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models?

Reyhaneh Hashemi, Pierre Brigode, Pierre-André Garambois, and Pierre Javelle

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Short summary
Hydrologists have long dreamed of a tool that could adequately predict runoff in catchments. Data-driven long short-term memory (LSTM) models appear very promising to the hydrology community in this respect. Here, we have sought to benefit from traditional practices in hydrology to improve the effectiveness of LSTM models. We discovered that one LSTM parameter has a hydrologic interpretation and that there is a need to increase the data and to tune two parameters, thereby improving predictions.
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