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

Data sets

Rock moisture in the YunGang Grottoes K. G. Ouyang and X. W. Jiang https://doi.org/10.5281/zenodo.7382895

Model code and software

LSTM+SHAP_Rock water content K. G. Ouyang and X. W. Jiang 0.5281/zenodo.7382827

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.