Landslide Susceptibility Prediction Using GIS and PSO-SVM

Landslide susceptibility prediction is the key technology in landslide monitoring, early warning, and assessment. The core problem in quantitative prediction of landslide hazards is the effective selection of conditioning factors and prediction models. In this paper, the Three Gorges Reservoir area was selected as a case study to predict landslide susceptibility. First, key landslide-related factors were selected as input variables using topographic, geological, and remote sensing data. Secondly, according to the nonlinear and uncertainty characteristics of landslides, a PSO-SVM model was trained and used to assess landslide susceptibility. Finally, the prediction results of grid-and object-based prediction models were validated by comparing them with known landslides using the classification accuracy. The results show that object-based PSO-SVM possesses high prediction accuracy with the area under curve of 0.841 5 and a Kappa coefficient of 0.849 0. These experimental results are consistent with field investigations and can provide a reference for landslide prevention and reduction in the Three Gorges, China.