Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 4
Articles | Volume 28, issue 4
https://doi.org/10.5194/hess-28-917-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-917-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 4
Research article
 | 
27 Feb 2024
Research article |  | 27 Feb 2024

A comprehensive study of deep learning for soil moisture prediction

Yanling Wang, Liangsheng Shi, Yaan Hu, Xiaolong Hu, Wenxiang Song, and Lijun Wang

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Short summary
LSTM temporal modeling suits soil moisture prediction; attention mechanisms enhance feature learning efficiently, as their feature selection capabilities are proven through Transformer and attention–LSTM hybrids. Adversarial training strategies help extract additional information from time series’ data. SHAP analysis and t-SNE visualization reveal differences in encoded features across models. This work serves as a reference for time series’ data processing in hydrology problems.
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