## 3 Materials and Methods
### Contributing Factors  
Landslide susceptibility evaluation indicators can be categorized into two primary types: internal and external factors [24]. Internal indicators refer to topographic and geomorphological data of the AOI, such as elevation, slope, and aspect, etc. External factors, often referred to as triggering factors, include variables like rainfall and seismic  
Figure 3: Landslides identified during field survey. activity. Depending on the characteristics of the dataset, these indicators can further be classified into continuous indicators (elevation, slope, etc.) and nominal indicators (stratigraphic lithology, land use type, etc.). Currently, there is no unified standard or specification for constructing landslide susceptibility evaluation indicator systems [21; 25; 26]. The selected evaluation indicators vary significantly across different regions. Table 1 compiles landslide susceptibility evaluation indicators from various studies, showing substantial differences in the selection and number of indicators. Most studies' evaluation systems consist solely of internal indicators (topographic and geomorphological indicators), with commonly used indicators being elevation, slope, aspect, and plan and profile curvature. These indicators can be computed from the Digital Elevation Model (DEM) of the study area using Geographic Information Systems (GIS) tools. The distance to rivers, distance to roads, and distance to faults is also frequently considered. Some studies use the distance to linear structures as an indicator. The concept of linear structures refers to significant landscape lines formed by joints, fractures, shear zones, and faults, which can more comprehensively reveal the topographic and geomorphological features of the study area [27]. Figure 4: Workflow of landslide susceptibility assessment along the G3033 Duku Expressway 2 km buffer zone. The selection of external (triggering) indicators was based on prominent disaster-inducing factors specific to the local area. Considering the meteorological and topographical characteristics as well as landslide mechanisms observed in the Tinshan region and supported by a comprehensive literature review, we initially selected 16 indicators: plain curvature, profile curvature, roughness, STI, TWI, elevation (DEM), aspect, slope, NDVI, land cover, distance to roads, distance to rivers, distance to faults, lithology, and precipitation (including rainfall and snowfall) as well as snowmelt. Precipitation and snowmelt were chosen as triggering indicators since they are recognized as primary landslide-inducing factors during the warm seasons in Xinjiang [32; 33]. The selection is also consistent with frequently used factors identified in previous studies, including a review by [PERSON] et al. [7], which summarized 45 landslide susceptibility indicators commonly used between 2008 and 2020, highlighting slope, land use/cover, aspect, elevation, geology, NDVI, distance to roads, rainfall, and distance to rivers as the most frequent. The digital elevation model (DEM) was acquired through the ASTER Global Digital Elevation Model V003 from [[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)) (accessed on 25 May 2025) with a resolution of 30 m. Plain curvature, profile curvature, roughness, STI, TWI, elevation (DEM), aspect, and slope were calculated and extracted in ArcMap 10.8 based on the DEM of our area of interest (AOI). The road and river datasets were obtained through the OpenStreetMap program at [[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org)) (accessed on 8 August 2024). The faults dataset was sourced from the results published in July 2024 by the research team led by [PERSON] et al. at the Institute of Geology [34], China Earthquake Administration. The multiple ring buffer function was employed to construct the distances to roads, rivers, and faults.