Fuzzy Logic Approach for Regional Landslide Susceptibility Analysis Constrained by Spatial characteristics of Landslide Disaster Environmental Factors

The complex geological conditions in the mountainous areas of western China, with strong internal and external dynamics effects, make catastrophic landslides frequent. The analysis of landslide susceptibility has become a necessary means for scientific early warning and active prevention before disasters. In the traditional landslide susceptibility analysis method, the general calculation accuracy of the single knowledge-driven model is limited, and the weight of the impact factor is highly subjective. The data-driven model also relies too much on the quality and quantity of sample data, and the heterogeneity of the landslide disaster environment is prominent. To overcome these shortcomings, this paper proposes a regional landslide susceptibility method that couples the contribution weight of landslide disaster environmental factors and heuristic knowledge fuzzy logic model.The proposed method uses spatial statistical indicators such as the historical landslide frequency ratio and the information entropy weight of the landslide disaster environmental factors to explicitly describe the contribution and spatial distribution characteristics of the landslide disaster environmental factors, which measures the constraint relationship and the mapping structure between multi-factors and landslides, and realizes multi-factor coupling regional landslide susceptibility. The experiment selects the disaster-prone areas in FengJie, Chongqing for verification and evaluation; and the result is superior to the single Information Value Model and Information Value and Logistic Regression model, which verifies the reliability of the method in this paper. The proposed method can overcome the strict requirements of the existing landslide susceptibility analysis on the number of historical observation samples, spatial representativeness and stable relationship of the obtained variables to a certain extent, and can provide reliable technical support for the susceptibility analysis of large-scale.