Articles | Volume 30, issue 10
https://doi.org/10.5194/hess-30-2973-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-2973-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 May 2026
Research article |
| 19 May 2026
| 19 May 2026
Interpretable soil moisture prediction with a knowledge-guided deep learning approach
Yanling Wang
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Yaan Hu
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, China
Leilei He
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Lijun Wang
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Wenxiang Song
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Liangsheng Shi
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
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Short summary
This study introduces a new interpretable deep learning method that accurately predicts multi-depth soil moisture simultaneously without physical assumptions. The model provides insights into soil properties, while delivering precise predictions across diverse scenarios. Tested under various conditions, it outperforms traditional approaches, particularly when enhanced with basic physics. This tool can help improve water management by offering reliable and efficient soil moisture forecasts.
This study introduces a new interpretable deep learning method that accurately predicts...
