Multi-model-based evaluation of landslide susceptibility in a meizoseismal area

On August 8, 2017, a magnitude 7 earthquake struck Jiuzhaigou County, Aba Prefecture, Sichuan Province, inducing a large number of landslides. Evaluating the susceptibility to landslides induced by strong earthquakes can provide a scientific basis for disaster risk management and monitoring. However, different evaluation models can obtain different spatial distributions of landslide susceptibility, and thus, selecting the optimal model is the most effective way to improve the susceptibility evaluation. To select the most suitable evaluation model for a strong earthquake area (Jiuzhaigou), 12 influencing factors affecting the landslide occurrence, including slope, elevation, and aspect, were extracted, and different statistical analysis methods and machine learning models were used to calculate the susceptibility index. The results show that the deep neural network model had the highest accuracy (85.4%), followed by the random forest and support vector machine models (84.2% and 82.3%, respectively), while the logistic regression model and certainty factor models achieved accuracies of 80.8% and 76.2%, respectively. Accordingly, the deep neural network model can be considered a new tool to achieve the more accurate zonation of landslide susceptibility in meizoseismal regions.