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Susceptibility assessment of landslides triggered by earthquakes in the Western Sichuan Plateau
- Cao, Juan, Zhang, Zhao, Wang, Chenzhi, Liu, Jifu, Zhang, Liangliang
- Catena 2019 v.175 pp. 63-76
- disaster preparedness, disasters, earthquakes, ergonomics, geophysics, infrastructure, landslides, models, mountains, prediction, regression analysis, tourism, tourists, China
- Seismic landslides are the most harmful natural events in mountainous areas worldwide. They are secondary disasters triggered by earthquakes and can cause a great number of casualties and significant damage to infrastructure. Three areas, Wenchuan, Lushan and Jiuzhaigou, which are highly prone to seismic landslides, were selected for this study. The aim of this study is to compare the prediction accuracy and spatial generalization ability of the logistic regression (LR) and random forest (RF) models in seismic landslide susceptibility mapping. First, using the LR and RF techniques, the susceptibility models of seismic landslides were developed in Wenchuan and Lushan based on thirteen influencing factors. Then, the accuracy of the susceptibility mapping was evaluated by the area under the curve (AUC) values of the receiver operating characteristic (ROC) curves. The results showed that both RF models have excellent prediction capability and strong robustness over large areas compared with the LR models. The AUC values of the RF and LR models were 0.811 vs. 0.946 and 0.905 vs. 0.969 in Wenchuan and Lushan, respectively. Second, we used the seismic landslides of Wenchuan and Lushan to develop the better model (RF), and then applied the developed RF model to produce the landslide susceptibility mapping of Jiuzhaigou County. The prediction accuracy for Jiuzhaigou dropped to 0.704. The results show that the RF model developed in Wenchuan and Lushan has robustness and spatial generalization ability over large areas. However, many landslides that had not been identified by the model, were located in the scenic area of Jiuzhaigou, a popular tourist destination. This suggests that intensive human activities could increase the landslide susceptibility. Thus, we added a new factor related to tourism to produce an improved RF model. The prediction accuracy of the altered model improved significantly, with the AUC value rising from 0.704 to 0.987. Thus, human engineering activities have a significant impact on landslides. Our study indicates that human engineering activities can increase the likelihood of landslides; hence, human engineering activities related to tourism should not be neglected in landslide susceptibility research. Appropriate disaster prevention and mitigation measures should be taken for the twelve geoparks of western China with peak ground acceleration ≥0.2 g.