Abstract: Three Gorges Reservoir is one of the landslide disaster-prone areas in China, and it is meaningful to as-sess the landslides susceptibility of Three Gorges Reservoir both for disaster prevention and reduction. The WI (Weighted-Information)-SVM(Support Vector Machine) model was adopted to assess the landslide susceptibility on the basis of entropy and SVM models. The SVM’s training dataset was comprised by the entropy of nine influence factors, including the stratum lithology, the geological structure, the slope gradient, the direction and structure of slope, the land use, the influence of water, and the NDVI (Normalized Difference Vegetation Index), together with the sum of them. The landslide susceptibility of the whole study area was evaluated, and the result of landslide susceptibility was ranked according to the zero value and abrupt change value of the decision value of model. The landslide susceptibility in Wanzhou district was assessed as an example to validate the WI-SVM model. The research result showed that the accuracy of the training dataset was 81.41% and verification dataset 91.11%, superior to commonly used models. Area with high and relatively high susceptibility accounts for 47.05% of the entire area, mainly in the mainstream and tributaries of the Yangtze River with strong human activities. The results are consistent with the distribution of landslides which has been known, indicating that the WI-SVM model has good applicability for the study area.

Key words: landslide hazards, weighted-information, support vector machine, susceptibility assessment, Three Gorges Reservoir area