Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 10
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.
https://doi.org/10.5194/hess-30-2973-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 10
Research article
 | 
19 May 2026
Research article |  | 19 May 2026

Interpretable soil moisture prediction with a knowledge-guided deep learning approach

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

Viewed

Total article views: 11,991 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
9,740 2,052 199 11,991 147 200
  • HTML: 9,740
  • PDF: 2,052
  • XML: 199
  • Total: 11,991
  • BibTeX: 147
  • EndNote: 200
Views and downloads (calculated since 18 Sep 2025)
Cumulative views and downloads (calculated since 18 Sep 2025)

Viewed (geographical distribution)

Total article views: 11,991 (including HTML, PDF, and XML) Thereof 11,833 with geography defined and 158 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 13 Jul 2026
Download
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.
Read more
Share