Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China

In recent decades, with the increase in extreme climate duration and the continuous development of urbanization in China, the threat of landslide disasters has become increasingly serious. More and more scholars pay attention to the problem of the prevention of landslide disasters. Therefore, the landslide susceptibility prediction is generated, which can play an important role in the design of land development and urban development schemes in mountainous areas. In this paper, the frequency ratio (FR) model is used to quantitatively analyze the relationship between each factor and the occurrence of landslide (elevation, slope, aspect, plan curvature, profile curvature, distance to faults, rainfall, distance to rivers, soil types, land cover, Normalized Difference Vegetation Index (NDVI) and distance to roads). Based on the analysis of landslide distribution, 12 influencing factors were selected to establish the landslide susceptibility evaluation index system. Historical landslide points were randomly divided into training (70% of the total) and validation (30%) sets. Thereafter, decision tree (DT), logistic regression (LR), and random forest (RF) models were used to generate the landslide susceptibility mapping (LSM), and the predictive performance of the three models was evaluated using receiver operating characteristic (ROC) curves. The FR model results showed that landslides mostly occurred at slopes of 0–15°, elevations of