# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Landslide Susceptibility  
Landslide hazard is defined as the probability of landslide occurrence of a specific type (\"when\" or \"how frequent\") and magnitude (\"how large\") [12]. The spatial probability of landslide hazard is assessed by carrying out landslide susceptibility mapping or popularly known as landslide hazard zonation mapping. Landslide susceptibility assessment determines potential of landslide event considering several predisposing factors and investigating their spatial distribution [12]. The spatial occurrence of landslides is controlled by several and sometimes inter-playing factors such as geological setting, rainfall, morphology, soil, and vegetation conditions, thus landslide susceptibility assessment is not a straightforward task, and many methodologies have been developed to assist the susceptibility analysis. The classification techniques used for landslide susceptibility models has also changed over time. We categorized them into four groups: Qualitative, Semi-quantitative, Quantitative, and Deterministic (Figure 7). In this section, we explore the models, the parameters used, and the trend in susceptibility analysis. Initially, studies were mostly based on qualitative methods wherein values are ascertained for landslide conditioning factors based on the knowledge about the study area and are quite subjective [69; 70]. Semi-quantitative methods are logical tools and emphasize the significant factors by assigning weights or values. These include techniques such as Analytic Hierarchy Process (AHP), danger pixel approach, and a weighted linear combination [71]. The deterministic analysis involves an analysis of physical and mechanical soil properties and determines the susceptibility in the form of a factor of safety (FS) [72]. FS is the ratio between factors affecting landslide to factors preventing landslide incidences. This involves the utilization of several factors such as infiltration, soil cohesion, groundwater table, pore water pressure, geotechnical soil parameters, etc. Several deterministic models are available to determine slope stability such as SHALSTAB (Shallow Landsliding Stability model), SINMAP (Stability Index Mapping), SHETRAN, TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope Stability) model, etc. In the case of the Indian Himalayas, [PERSON] et al. [72] used SHALSTAB model to determine the critical steady-state rainfall for slope stability in Darjeeling Himalayas. A landslide inventory map was developed using various satellite images (Resourcesat -1, Cartosat -1, and Google Earth) and field surveys, soil parameters (depth, saturated soil density, engineering properties, and soil transmissivity), and slope parameters (angle, contour length, and upslope contributing area). [PERSON] et al. [73] determined FS for terrain stability for Garigao watershed area of Uttarakhand by coupling an infinite slope stability model with a steady-state hydrological model (LIDA) using the spatial analysis in GIS environment. The landslide locations were mapped using LISS IV and Cartosat-1 PAN data and the input parameters used were soil texture, index properties, porosity, LULC, and terrain properties. The results indicate an increase in the unstable areas of more than 45% for rainfall intensity variation from 50 to 100 mm/day. Figure 7: Types of modeling techniques used in landslide susceptibility studies. Quantitative models depend on the landslide density under each influencing factor and can be further classified as a bivariate and multivariate [74]. Both bivariate and multivariate statistical models compute weights; however, multivariate techniques depend on the collective effect of parameters [75; 76]. Bivariate statistical methods include weights of evidence, frequency ratio, information value, and the combination of frequency ratio and fuzzy methods [77; 78; 79]. The most popular multivariate model used is logistic regression [80].