Landslide Susceptibility Mapping Considering the Landslide Spatial Aggregation Using Dual Frequency Ratio Method: A Case Study on the Middle Reaches of Tarim River Basin

The phenomenon of landslide spatial aggregation is widespread in nature, which can affect the result of landslide susceptibility prediction (LSP). In order to eliminate the uncertainty caused by landslide spatial aggregation in LSP study, scholars have proposed some methods to quantify the degree of landslide spatial aggregation, such as class landslide aggregation index (LAI), which is widely used. However, due to the limitations of the existing LAI method, it is still uncertain when applied to the LSP study of the area with complex engineering geological conditions. Considering the landslide spatial aggregation, a new method, dual frequency ratio (DFR), was proposed in this paper to establish the relationship between the landslide occurrence and twelve predisposing factors (namely, slope, aspect, elevation, relief amplitude, engineering geological rock group, fault density, river density, annual average rainfall, NDVI, distance to road, quarry density and hy-dropower station density). And in the DFR method, an improved LAI was used to quantify the degree of landslide spatial aggregation in the form of frequency ratio. Taking the middle reaches of Tarim River Basin as the study area, the application of DFR method in LSP study was verified. Meanwhile, four models were adopted to calculate the landslide susceptibility indexes (LSIs) in this study, including frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR) and random forest (RF). Finally, the prediction performance of each LSP model was evalu-ated by the receiver operating characteristic curves (ROCs) and distribution patterns of LSIs. The results showed that DFR method could reduce the adverse effect of landslide spatial aggregation on LSP study and enhance the prediction performance of LSP model better. In addition, models of LR and RF had superior prediction performance, among which DFR-RF model had the highest prediction accuracy value and quite reliable result of LSIs.