Articles | Volume 26, issue 1
https://doi.org/10.5194/nhess-26-487-2026
© 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/nhess-26-487-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
26 Jan 2026
Review article |
| 26 Jan 2026
| 26 Jan 2026
Review article: Deep learning for potential landslide identification: data, models, applications, challenges, and opportunities
Pan Jiang
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China
Zhengjing Ma
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China
Gang Mei
CORRESPONDING AUTHOR
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China
Related authors
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Kai-Gao Ouyang, Xiao-Wei Jiang, Gang Mei, Hong-Bin Yan, Ran Niu, Li Wan, and Yijian Zeng
Hydrol. Earth Syst. Sci., 27, 2579–2590, https://doi.org/10.5194/hess-27-2579-2023, https://doi.org/10.5194/hess-27-2579-2023, 2023
Short summary
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.
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Short summary
In order to elucidate the potential for integrating deep learning with potential landslide identification, this paper focuses on four key dimensions: (1) Summarising data sources for potential landslide identification. (2) Compare the roles of commonly used deep learning models. (3) Analyse the practical applications of deep learning in early landslide detection. (4) Investigate key challenges and propose future priorities for potential landslide identification.
In order to elucidate the potential for integrating deep learning with potential landslide...
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