Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 17
Articles | Volume 28, issue 17
https://doi.org/10.5194/hess-28-4187-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-28-4187-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 17
Opinion article
 | 
12 Sep 2024
Opinion article |  | 12 Sep 2024

HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin

Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing

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Short summary
Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
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