Landslide susceptibility prediction by gray wolf optimized support vector machine model under different factor states

Abstract

Landslide susceptibility prediction (LSP) is crucial for hazard prevention and geological risk assessment. Support vector machine (SVM) is widely used for LSP, but its parameter optimization problem affects the prediction accuracy and generalization ability of the model, and variations in parameter combinations may result in different prediction outcomes, which brings some challenges to the application of the model. We present a procedure for LSP using the gray wolf optimization (GWO) algorithm to optimize SVM models in the Sanshui District, Foshan City, China. Fifteen factors affecting landslide susceptibility are selected and processed by the natural breakpoint method and normalization method. To prevent overfitting and improve the generalization ability of the model, the five factors with high correlation are excluded using the Pearson correlation coefficient. The grid search method and GWO are used to optimize the SVM parameters and establish the GWO-SVM model. The results indicated that the GWO-SVM model, which incorporated the normalization method (referred to as GWO-SVM-NOR), demonstrated superior predictive accuracy, achieving an impressive area under the curve value of 0.886. The gray wolf algorithm improves the fitting accuracy of SVM and optimizes the model prediction performance with better stability, which is suitable for predicting areas susceptible to landslides.