Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 3
Articles | Volume 29, issue 3
https://doi.org/10.5194/hess-29-785-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-785-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 3
Research article
 | 
13 Feb 2025
Research article |  | 13 Feb 2025

A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting

Everett Snieder and Usman T. Khan

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Short summary
Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine learning (ML) models are increasingly being applied for flood forecasting. Such models are typically trained on large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets that maximise the spatio-temporal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
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