Analysis of Optimal Buffer Distance for Linear Hazard Factors in Landslide Susceptibility Prediction

A linear hazard-causing factor is the environmental element of landslide susceptibility prediction, and the setting of buffer distance of a linear hazard-causing factor has an important influence on the accuracy of landslide susceptibility prediction based on machine learning algorithms. A geographic information system (GIS) has generally been accepted in the correlation analysis between linear hazard-causing factors and landslides; the most common are statistical models based on buffer zone analysis and superposition analysis for linear causative factor distances and landslide counts. However, there is a problem in the process of model building: the buffer distance that is used to build the statistical model and its statistical results can appropriately reflect the correlation between the linear disaster-causing factors and landslides. To solve this problem, a statistical model of landslide density and distance of linear disaster-causing factors under different single-loop buffer distances was established based on Pearson’s method with 12 environmental factors, such as elevation, topographic relief, and distance from the water system and road, in Ruijin City, Jiangxi Province to obtain the most relevant single-loop buffer distance linear disaster-causing factor combinations; random forest (RF) machine learning models were then used to predict landslide susceptibility. Finally, the Kappa coefficient and the distribution characteristics of the susceptibility index were used to investigate the modeling laws. The analysis results indicate that the prediction accuracy of the most correlated single-loop buffer distance combination reaches 96.65%, the error rate of non-landslide points is 4.2%, and the error of landslide points is 11.3%, which is higher than the same single-loop buffer distance combination, confirming the reasonableness of the method of using correlation to obtain the linear disaster-causing factor buffer distance.