Landslide Susceptibility Mapping along the Anninghe Fault Zone in China using SVM and ACO-PSO-SVM Models

Abstract

Abstract In the present study, a hybrid machine learning model was designed by integrating ant colony optimization (ACO), particle swarm optimization (PSO), and support vector machine (SVM) algorithms. The model was used to map the landslide susceptibility of the Anninghe fault zone in Sichuan Province, China. Based on this, 12 conditioning factors associated with landslides were considered, namely, altitude, slope angle, cutting depth, slope aspect, relief amplitude, stream power index (SPI), gully density, lithology, rainfall, road density, distance to fault, and peak ground acceleration (PGA). The overall performance of the two resulting models was tested using the receiver operating characteristic (ROC), area under the ROC curve (AUC), Cohen’s kappa coefficient, and five statistical evaluation measures. The success rates of the ACO-PSO-SVM model and the SVM model were 0.898 and 0.814, respectively, while the prediction rates of the two models were 0.887 and 0.804, respectively. The results show that the ACO-PSO-SVM model yields better overall performance and accurate results than the SVM model. Therefore, in conclusion, the ACO-PSO-SVM model can be applied as a new promising method for landslide susceptibility mapping in subsequent studies. The results of this study will be useful for land-use planning, hazard prevention, and risk management.