Landslide Susceptibility Using Weighted Regression Model: A Geo-spatial Approach

Steep geographical terrain is very vulnerable to the trend of landslip all around the world. Landslides take place regularly and on a yearly basis throughout India’s diverse hill and mountain ranges. Tamil Nadu’s Nilgiris district is especially susceptible to landslides because of the region’s abundant rainfall for the duration of the South and North East monsoons. The focus of this study is to define high-risk areas and pinpoint features that make landslides vulnerable. It is essential to use the landslide hazard zonation maps appropriately and conduct a thorough analysis of each slope that is vulnerable to landslides. Development, the risk of landslides today, and projected future slopes where we can place the early warning system for slope failure based on past landslide locations should all be represented on planning-level maps. Using the weighted regression model, a straightforward statistical method has been used to calculate the proximity of their relationship. Additionally, weighted regression models were useful in validating the chosen causal factors based on their capacity to prevent a landslide episode because they could explain in detail the variance in scores between the causative factors for each class of landslides as well as the distribution of landslides using geographic information systems (GIS). The outcome indicates a relationship between the likelihood of a landslip occurring and the slope, with steeper slopes having higher landslip probability and a slope prediction rate of 23.82. The research area’s western and eastern halves are heavily populated with high-susceptibility areas.