Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 23
Articles | Volume 29, issue 23
https://doi.org/10.5194/hess-29-6811-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-6811-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 23
Research article
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 | 
01 Dec 2025
Research article | Highlight paper |  | 01 Dec 2025

From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction

Jiangtao Liu, Chaopeng Shen, Fearghal O'Donncha, Yalan Song, Wei Zhi, Hylke E. Beck, Tadd Bindas, Nicholas Kraabel, and Kathryn Lawson

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Machine learning is used widely in hydrological research nowadays, but benchmarking them for various applications was lacking. This paper addresses the question which machine learning model to be used for which application and why.
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Short summary
Using global and regional datasets, we compared attention-based models and Long Short-Term Memory (LSTM) models to predict hydrologic variables. Our results show LSTM models perform better in simpler tasks, whereas attention-based models perform better in complex scenarios, offering insights for improved water resource management.
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