| Issue |
E3S Web Conf.
Volume 198, 2020
2020 10th Chinese Geosynthetics Conference & International Symposium on Civil Engineering and Geosynthetics (ISCEG 2020)
|
|
|---|---|---|
| Article Number | 02024 | |
| Number of page(s) | 3 | |
| Section | Structural Engineering Monitoring, Control, Repair and Reinforcement | |
| DOI | https://doi.org/10.1051/e3sconf/202019802024 | |
| Published online | 26 October 2020 | |
Comparison of three statistical methods for earthquake-induced landslides susceptibility in Lushan region China
1 College of Geophysics, Chengdu University of Technology, Sichuan 610059, China
2 Key Lab of Earth Exploration and Information Techniques of Ministry Education, Chengdu University of Technology, Sichuan 610059, China
3 Dongpo district, Meishan, Sichuan province, Meishan Housing and Urban-Rural Construction Bureau, Sichuan 620000, China
* This paper adopts three models including the logistic regression (LR), support vector machine (SVM), and random forest (RF) to study the susceptibility distribution rule of susceptibility distribution of earthquakes induced landslides. The Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and Ratio were used for evaluating the model’s accuracy and mapping availability susceptibility assessment. The result shows that RF has the best performance in the susceptibility assessment of earthquake-induced landslides in the Lushan region of China.
This paper adopts three models including the logistic regression (LR), support vector machine (SVM), and random forest (RF) to study the susceptibility distribution rule of susceptibility distribution of earthquakes induced landslides. The Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and Ratio were used for evaluating the model’s accuracy and mapping availability susceptibility assessment. The result shows that RF has the best performance in the susceptibility assessment of earthquake-induced landslides in the Lushan region of China.
© The Authors, published by EDP Sciences 2020
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