Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation: a case study of the upper Jinsha River

Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation: a case study of the upper Jinsha River

AbstractLandslide susceptibility mapping (LSM) comprehensively evaluates the spatial probability of landslide occurrence by using different environmental factors. However, most of the evaluation methods ignore the dynamic characteristic factors of landslides, which makes it difficult to obtain relia...

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Bibliographic Details
Main Authors: Jiaming Yao, Xin Yao, Zheng Zhao, Xinghong Liu
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
Landslide susceptibility
InSAR
machine learning
random forest
Jinsha River
Online Access:https://www.tandfonline.com/doi/10.1080/19475705.2023.2212833

Internet

https://www.tandfonline.com/doi/10.1080/19475705.2023.2212833

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