Abstract:Landslides are frequent geological disasters in China, landslide susceptibility evaluation involves many influencing factors. How to use multiple influencing factors to conduct accurate and effective landslide susceptibility evaluation is the key and premise of landslide disaster reduction and prevention. In order to discuss the applicability of different landslide susceptibility evaluation methods based on BP neural network model, Pujiang County in western Sichuan were took as the study area, and 12 types of impact factors such as geology, landform and environment were chose after field survey and cataloging, the correlation between each impact factor and landslide were analyzed, the weight of the impact factor were determined, the BP neural network model were constructed, and the preparation and accuracy evaluation of landslide susceptibility evaluation map by factor weight method and grid assignment method were completed. The results show that there are no linear correlations among the 12 types of landslide influencing factors selected in the study area, and the slope, TWI and distance from the road have obvious effects on the landslide development in the area. The BP neural network model constructed by the landslide influencing factors can effectively carry out the quantitative evaluation of landslide susceptibility. Based on the field investigation and ROC accuracy analysis, the grid assignment method based on BP neural network model (AUC value is 0.86) is superior to the factor weight method (AUC value is 0.798) in evaluation accuracy. The grid assignment method based on BP neural network model is more suitable for landslide susceptibility evaluation in the study area.