## 3 Methods  
The landslide susceptibility maps were prepared using VR and RIRA models to assess the landslide susceptibility in the Yangou catchment. Figure 2 showed the flow chart of the application method, including the data collection, gathering of the landslide conditioning factors, determination of relationship between each condition factors' subclasses, and landslide inventory map using VR, application of the RIRA method in the landslide sensibility, landslide susceptibility assessment of numerical data integration, and landslide susceptibility mapping. The landslide inventory database, which included 27 locations, volumes, and types were provided by the Xi'an Geological Survey Center of the China Geological Survey. The landslide is divided into four sections: \(\leq\)1 \(\times\) 104, 1 \(\times\) 104-10 \(\times\) 104, 10 \(\times\) 104-100 \(\times\) 104, and \(\geq\)100 \(\times\) 104 m3, corresponding to tiny, small, medium, and large landslides. The amount of the landslide and its percentage in each interval were counted. In this study, nine parameters were chosen according to the literature review and the general environment of the study area. These conditioning factors are land-use, slope, profile curvature, altitude/elevation, distance to roads, distance to rivers, lithology, normalized difference vegetation index (NDVI), and rainfall. The altitude/elevation, slope, and profile curvature were extracted from the Digital Elevation Model (DEM) (cell size: 25 m \(\times\) 25 m), which is converted from a topographic map with a scale of 1:100,000. They were also collected from the \"Xi'an Geological Survey Center of China Geological Survey\". The NDVI was extracted from the Landsat 8/OLI data set, which is provided by the Geospatial Data Cloud site, Computer Network Information Center [23]. The computational formula of NDVI is  
\[\text{NDVI}=(NIR-R)/(NIR+R) \tag{1}\]  
where _NIR_ is the reflection value of the near-infrared band, and \(R\) is the reflection value of the red band. The landuse data were provided by the Resource and Environment Science and Data Center [24]. It was decided that the study area was mainly covered by eight  
Figure 2: Flowchart of the used methodology. land-use types, namely water, wetland, farmland, resident land, bareland, forest, shrubland, and grassland. The lithology of the Yangou Watershed was delineated using the China Geological Map at a 1:250,000-scale. The distance to rivers was extracted using hydrological analysis of DEM and neighborhood statistic in the ArcGIS. The distance to roads was developed using China Road Network data at a 1:100,000-scale. The rainfall data were from the China meteorological background data set of a 500 m \(\times\) 500 m pixel size, which is provided by the Resource and Environment Data Cloud Platform [25]. Then, these nine conditioning factors were selected in the present study and were standardized to the same size of 25 m \(\times\) 25 m for further analyses. The study area covers 355 columns and 447'rows. The spatial analysis tool in the ArcGIS software was used to count the corresponding values of each factor at the landslide location. Also, the occurrence frequency ratio of the different classes was calculated in the land use and geology as the corresponding values of the landslide. To show the relationship between each factor's subclasses and landslide volumes, the VR approach is proposed. It is a variant of the probabilistic method that is based on the observed relationships between the volume of the landslide and each landslide conditioning factor. The volume ratios for the classes or types of each conditioning factor were calculated by dividing the landslide volume by the typical value ratio of the study area.