Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation: a case study of the upper Jinsha River-Reference-Cited by-同舟云学术

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

Published:2023-06-06 Issue:1 Volume:14 Page:
ISSN:1947-5705
Container-title:Geomatics, Natural Hazards and Risk
language:en
Short-container-title:Geomatics, Natural Hazards and Risk

Author:

Yao Jiaming¹²³,Yao Xin¹²,Zhao Zheng³,Liu Xinghong¹²

Affiliation:

1. Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China

2. Key Laboratory of Active Tectonics and Geological Safety, Ministry of Natural Resources, Beijing, China

3. University of Chinese Academy of Sciences, Beijing, China

Funder

China Three Gorges Corporation

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

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

Reference74 articles.

1. Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

2. A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset

3. Landslide mapping using object-based image analysis and open source tools

4. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

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