## 2 Materials and Methods
### Selection of Landslide Influencing Factors (LIFs)  
The accuracy of susceptibility maps is highly affected by LIFs. There are no universal criteria for selecting conditional factors due to the complex nature of the geoenvironment in different areas [25]. The selection of these LIFs largely depends on the climatic, geological, and topographical condition of the area; therefore, considering the topography, LCLU, and geological conditions of the study area, a total of 12 LIFs (Table 1, Figure 5) including elevation, slope, aspect, TWI, TPI, distance to drainage, distance to fault, distance to road, NDVI, rainfall, LCLU, and geological layers were prepared. All the influencing factors were resampled at a standard pixel size of 30 m. The details of the chosen influencing factors are discussed in Table 1 and their spatial patterns and distribution are presented in Figure 5.  
Figure 4: Photographs of events: (**a**) landslide along KKH, (**b**) GE visualization of this event.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Influencing Factor** & **Input Data** & **Description** \\ \hline
1 & Elevation & SRTM DEM & Digital elevation of the terrain surface. Values vary between 279 and 5934 m (Figure 5a). \\ \hline
2 & Slope & SRTM DEM & A most crucial parameter that has a direct influence on slope failure and susceptibility. Values are up to 89 degrees (Figure 5b). \\ \hline
3 & Aspect & SRTM DEM & The exposure of the slope to conditions such as sunlight, temperature, and winds. An important causative factor to susceptibility (Figure 5c). \\ \hline
4 & TWI & SRTM DEM & TWI [48] is an index used to quantify topographic control of hydrological processes. Values vary from \(-\)3.7 to 16.43 (Figure 5d). \\ \hline
5 & TPI & SRTM DEM & TPI [48] is an index that reflects the morphology of the topography (Figure 5e). \\ \hline
6 & Distance to drainage & Topographic Sheet & Rivers and natural streams play an important role in slope failure due to the accumulation of water in the surrounding surface and subsurface. The proximity layer was generated based on Euclidean distance [49]. Values vary from 0 to 50,000 m (Figure 5f). \\ \hline
7 & Distance to fault & Geological Map & The strength of rocks/soil decreases with the presence of faults and lineaments. The fault lines were extracted from input datasets and the proximity layer was calculated based on Euclidean distance [49]. Values vary from 0 to 7250 m (Figure 5g). \\ \hline
8 & Distance to road & Topographic Sheet & To evaluate the effects of road engineering, this proximity layer was calculated based on Euclidean distance [49]. Values vary from 0 to 30,000 m (Figure 5h). \\ \hline
9 & NDVI & Landsat-8 OLI & NDVI [50] illustrates the density and spread of vegetation in contrast with the non-vegetated land. It is an important factor that exploits the relationship between landslide occurrence and vegetation cover density. Values range between \(-\)1 and +1 (Figure 5i). \\ \hline
10 & Rainfall & Topographic Rainfall Mission (TRMM)/GPM & Monthly averaged data product for the year 2017 was downloaded and used for the assessment with other factors. As discussed in Section 2.1 on the study area, the two climatic zones (north and south) can easily be identified in this rainfall map (Figure 5j). \\ \hline
11 & LCLU & Landsat-8 OLI & The object-based image analysis (ODIA) technique [51] was adopted to extract 8 LCLU classes with an accuracy of 86\% (Figure 5k). \\ \hline
12 & Geological layer & Geological Map & Fourteen different units of geology were identified. The distribution and details of these units can be found in Figure 5l. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic details of LIFs.  
Figure 5: Maps of the Landslide Influencing Factors: (**a**) elevation, (**b**) slope, (**c**) aspect, (**d**) TWI, (**e**) TPI, (**f**) distance to drainage, (**g**) distance to fault, (**h**) distance to road, (**i**) NDVI, (**j**) rainfall, (**k**) LCLU, and (**l**) geology.