## 3 Materials and Methods
### Data Preparation
It is expressed as \(\text{TWI}=\ln(\frac{\alpha}{\tan\beta})\), where \(\beta\) is the slope angle (radian), and \(\alpha\) is the flow accumulation through a point [72]. The TWI values of the study area can be calculated by GIS software and reclassified as (<4), (4-5), (5-6), (6-7), and (>7) with an interval of 1 (Figure 3f). SPI can directly measure the erosion capacity of the stream. A higher SPI value indicates that the stream has more powerful erosion on the slope surface [55]. The SPI values are mainly determined as \(\text{SPI}=\alpha\tan\beta\)[54; 70]. In this study, the SPI values were identified as five categories with an interval of 20, namely, (<20), (20-40), (40-60), (60-80), and (>80) (Figure 3g). As another topographic index, STI has also been considered to construct the landslide susceptibility model [73]. Similar to SPI, STI can quantitatively reflect the regional topographic features and erosion conditions [74]. For the present study, STI values contained five categories with an interval of 10: (<10), (10-20), (20-30), (30-40), (>40) (Figure 3h). Rivers can not only affect the moisture distribution in slopes, but can also erode the toes of slopes, which cause slope deformation and failure [75]. Thus, it is necessary to consider the river effects when producing landslide susceptibility maps. In this study, based on the distance to rivers, five buffer zones with an interval of 200 m were generated for each river: (<200 m), (200-400 m), (400-600 m), (600-800 m), and (>800 m) (Figure 3i). Generally speaking, road construction in mountainous areas, which always produce an engineering load and destroy the integrity of slope structure, have significant negative impacts on the slope stability [76]. Hence, the distance to roads is usually selected as a conditioning factor to embody the influence of road engineering activities on landslide occurrence [77]. Here, values of the distance to roads were divided into five groups with an interval of 300 m, i.e., (\(<\)300 m), (300-600 m), (600-900 m), (900-1200 m), and (\(>\)1200 m) (Figure 3j). Fault structures affect the spatial distribution and characteristics of landslides in a certain region [50]. According to relevant studies [30,78], the integrity of rock and soil mass generally decrease as the distance to the faults shorten. In this way, landslide hazards are more likely to occur in the neighboring area of faults. Ultimately, buffers of various faults in the study area were obtained and reclassified into five categories with an interval of 1000 m: (\(<\)1000 m), (1000-2000 m), (2000-3000 m), (3000-4000 m), and (\(>\)4000 m) (Figure 3k). In terms of soil, this is an essential factor that has a strong correlation with landslide occurrence [79]. To a great extent, the strength, root cohesion, permeability, and vegetation coverage of the soil mass depend on the soil type [80,81], which can impact the failure characteristics of slopes [82,83]. In this study area, a total of nine soil types were identified including cumultic anthrosol, dystric cambisol, eutric cambisol, calcaric fluvisol, haplic luvisol, chromic luvisol, eutric planosol, calcaric regosol, and eutric regosol (Figure 3l). Land use is one of the most frequently used conditioning factors, and the correlation between landslides and land use has been confirmed [84]. For instance, in some farmland regions, landslides are frequent and common under long-term irrigation [85]. For the study area, the types of land use mainly consist of farmland, forestland, grassland, water, residential areas, and bareland (Figure 3m). NDVI is a very popular index to measure the degree of vegetation in a region. NDVI values can be figured out by the formula NDVI = (I - R)/(IR + R), where IR is the infrared band and R is the red band of the electromagnetic spectrum [86]. The range of NDVI values is from \(-\)1 to 1, and a positive value means that the local ground is covered by vegetation.