# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Landslide Susceptibility
The analysis shows that most studies have used quantitative techniques, of which logistic regression has been most used [81; 82; 83]. Thereafter, frequency ratio and information value were used 16% and 9% of the studies, respectively [74; 84; 85; 86; 87; 88]. In terms of semi-quantitative models, AHP has been the most used method (Figure 8). After understanding the various models being used to determine susceptibility, the analysis of the parameters being used was investigated. The use of parameters for susceptibility analysis would affect the results; however, they usually depend on the local factors along with data availability. In addition, the factors depend on the type of movement, scale of study and the methodology used [89]. Based on the analysis of the articles, the factors influencing landslide susceptibility can be divided into four groups: (i) geological (lithology, geological structures); (ii) geomorphological (drainage, relative relief, slope, and slope aspect); (iii) environmental (land cover); and (iv) anthropogenic (roads). In the 81 studies, 36 factors were used, where some factors such as \"gradient\" and \"slope\" were clubbed together, and a word map of the factors is illustrated in Figure 9. The most used factors were slope angle (93.8%), land use/land cover (LULC) (92.5%), aspect (80.2%), and lithology (62.9%). Apart from these factors, another set of parameters, namely drainage density, curvature, topographic wetness index (TWI), stream power index (SPI), relative relief, lineament distance, and lineament density, was also utilized. Seismic factors, fault buffer, and road buffer have been less frequently used. The trend over the years in the number of factors to be used has increased with an average of six factors being used until 2010 and an average of eight factors thereafter. This trend can be ascertained to the increase in the availability of data and remote sensing products. Figure 8: Model types and the techniques used in hazard and susceptibility studies. In terms of the spatial resolution of the dataset used to determine susceptibility, the most commonly used Digital Elevation Model (DEM) is the one released by USGS, with a spatial resolution of 30 m (46%). Only 28% of the studies used a spatial resolution higher than 10 m, which was obtained from Cartosat-1 images, whereas 26% of studies used the dataset with a spatial resolution of 12.5 m from ALOS-PALSAR DEM. The scale of geological maps has generally been 1:250,000 (62%) while the remaining 38% studies were conducted using 1:50,000 scale. [PERSON] [69] formulated the guidelines for landslide hazard assessment based on Landslide Hazard Evaluation factor (LHEF) using factors such as structure, relative relief, lithology, slope morphometry, land use, and groundwater conditions. The LHEF rating system was based on determining the factors and assigning values based on the understanding/knowledge about the study region. The national standard body of the country, Bureau of Indian Standards (BIS), prepared its guidelines (BIS, 1998) based on [PERSON]'s study using the same factors excluding groundwater conditions [90]. BIS recommends an indirect method for medium-scale (1:25,000-1:50,000) landslide susceptibility mapping. Such technique is a generalized method that could be applied over large regions regardless of the relationship between causative factors and landslide types, often leading to moderate to poor predictions [91]. Following this, the nodal agency, Geological Survey of India (GSI), developed its own guidelines (GSI, 2005) considering 10 factors, which is a modified version of BIS (1998). [PERSON] et al. [90] compared the zonation maps prepared using BIS and GSI guidelines for Arunachal Pradesh and found that GSI guidelines were better than BIS guidelines. [PERSON] et al. [92] studied homogenous susceptible units (HSU)-based landslide hazard utilizing spatiotemporal data and landslide size, which could better represent homogeneous susceptible regions.