Comparison of LR,5-CV SVM,GA SVM,and PSO SVM for landslide susceptibility assessment in Tibetan Plateau area,China

The applicability of statistics-based landslide susceptibility assessment methods is affected by the number of historical landslides.Previous studies have proposed support vector machine(SVM)as a small-sample learning method.However,those studies demonstrated that different parameters can affect model performance.We optimized the SVM and obtained models as 5-fold cross validation(5-CV)SVM,genetic algorithm(GA)SVM,and particle swarm optimization(PSO)SVM.This study compared the prediction performances of logistic regression(LR),5-CV SVM,GA SVM,and PSO SVM on landslide susceptibility mapping,to explore the spatial distribution of landslide susceptibility in the study area in Tibetan Plateau,China.A geospatial database was established based on 392 historical landslides and 392 non-landslides in the study area.We used 11 influencing factors of altitude,slope,aspect,curvature,lithology,normalized difference vegetation index(NDVI),distance to road,distance to river,distance to fault,peak ground acceleration(PGA),and rainfall to construct an influencing factor evaluation system.To evaluate the models,four susceptibility maps were compared via receiver operating characteristics(ROC)curve and the results showed that prediction rates for the models are 84%(LR),87%(5-CV SVM),85%(GA SVM),and 90%(PSO SVM).We also used precision,recall,F1-score and accuracy to assess the quality performance of these models.The results showed that the PSO SVM had greater potential for future implementation in the Tibetan Plateau area because of its superior performance in the landslide susceptibility assessment.