Articles | Volume 27, issue 14
https://doi.org/10.5194/hess-27-2579-2023
https://doi.org/10.5194/hess-27-2579-2023
Research article
 | 
17 Jul 2023
Research article |  | 17 Jul 2023

Physics-informed machine learning for understanding rock moisture dynamics in a sandstone cave

Kai-Gao Ouyang, Xiao-Wei Jiang, Gang Mei, Hong-Bin Yan, Ran Niu, Li Wan, and Yijian Zeng

Viewed

Total article views: 2,202 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,606 519 77 2,202 63 47 44
  • HTML: 1,606
  • PDF: 519
  • XML: 77
  • Total: 2,202
  • Supplement: 63
  • BibTeX: 47
  • EndNote: 44
Views and downloads (calculated since 05 Dec 2022)
Cumulative views and downloads (calculated since 05 Dec 2022)

Viewed (geographical distribution)

Total article views: 2,202 (including HTML, PDF, and XML) Thereof 2,143 with geography defined and 59 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
Our knowledge on sources and dynamics of rock moisture is limited. By using frequency domain reflectometry (FDR), we monitored rock moisture in a cave. The results of an explainable deep learning model reveal that the direct source of rock moisture responsible for weathering in the studied cave is vapour, not infiltrating precipitation. A physics-informed deep learning model, which uses variables controlling vapor condensation as model inputs, leads to accurate rock water content predictions.