Landslide susceptibility assessment is a precondition of early warning and evaluation for regional landslide. Effective selection of hazard-inducing factors and establishment of assessment model are challenging in the prediction of landslide hazards. On the basis of the fusion of multi-source data including digital elevation model (DEM), geological map, road network map, and remote sensing image of Fugu County as a case study, the environmental factors such as landform and geomorphology, formation lithology and ground cover as well as inducing factors such as rainfall and human engineering activity were extracted as assessment indicators. The correlations among the extracted factors were analyzed and the topographic relief factor was eliminated. Furthermore, the particle swarm optimization (PSO) algorithm was adopted to optimize the parameters of support vector machine(SVM) model. The optimal parameters (penalty parameter c=1.42 and kernel parameter σ=1.15) were incorporated into the SVM model to establish the PSO-SVM model for landslide susceptibility assessment. The performance of the model was tested by the receiver operate curve (ROC) and Kappa coefficient, and results revealed that the success rate and the prediction rate of the PSO-SVM model were 0.931 and 0.917, respectively, and the prediction accuracy of train data and test data were 79.17% and 76.67%, respectively.
Key words
landslide /
assessment factors /
PSO-SVM model /
susceptibility assessment /
ROC /
Kappa coefficient
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