A physics-informed machine learning solution for landslide susceptibility mapping based on three-dimensional slope stability evaluation
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11771-024-5687-3.pdf
Reference53 articles.
1. HE Jian-yin, QIU Hai-jun, QU Fei-hang, et al. Prediction of spatiotemporal stability and rainfall threshold of shallow landslides using the TRIGRS and Scoops3D models [J]. Catena. 2021, 197: 104999. DOI: https://doi.org/10.1016/j.catena.2020.104999.
2. WANG Lin, WU Chong-zhi, YANG Zhi-yong, et al. Deep learning methods for time-dependent reliability analysis of reservoir slopes in spatially variable soils [J]. Computers and Geotechnics. 2023, 159: 105413. DOI: https://doi.org/10.1016/j.compgeo.2023.105413.
3. WANG Yun-hao, WANG Lu-qi, LIU Song-lin, et al. A comparative study of regional landslide susceptibility mapping with multiple machine learning models [J]. Geological Journal. 2023. DOI: https://doi.org/10.1002/gj.4902.
4. TANG Hui-ming, WASOWSKI J, JUANG C. Geohazards in the three Gorges Reservoir Area, China Lessons learned from decades of research [J]. Engineering Geology. 2019, 261: 105267. DOI: https://doi.org/10.1016/j.enggeo.2019.105267.
5. DENG Mao-lin, HUANG Xiao-hu, YI Qing-lin, et al. Fifteen-year professional monitoring and deformation mechanism analysis of a large ancient landslide in the Three Gorges Reservoir Area, China [J]. Bulletin of Engineering Geology and the Environment. 2023, 82(7): 243. DOI: https://doi.org/10.1007/s10064-023-03262-9.
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