Assessing Landslide Susceptibility along India’s National Highway 58: A Comprehensive Approach Integrating Remote Sensing, GIS, and Logistic Regression Analysis

The NH 58 area in India has been experiencing an increase in landslide occurrences, posing
significant threats to local communities, infrastructure, and the environment. The growing need to
identify areas prone to landslides for effective disaster risk management, land use planning, and
infrastructure development has led to the increased adoption of advanced geospatial technologies
and statistical methods. In this context, this research article presents an in-depth analysis aimed at
developing a landslide susceptibility zonation (LSZ) map for the NH 58 area using remote sensing,
GIS, and logistic regression analysis. The study incorporates multiple geo-environmental factors
for analysis, such as slope aspect, curvature, drainage density, elevation, fault distance, flow accumulation,
geology, geomorphology, land use land cover (LULC), road distance, and slope angle.
Utilizing 50% of the landslide inventory data, the logistic regression model was trained to determine
correlations between causal factors and landslide occurrences. The logistic regression model was then
employed to calculate landslide probabilities for each mapping unit within the NH 58 area, which
were subsequently classified into relative susceptibility zones using a statistical class break technique.
The model’s accuracy was verified through ROC curve analysis, resulting in a 92% accuracy rate. The
LSZ map highlights areas near road cut slopes as highly susceptible to landslides, providing crucial
information for land use planning and management to reduce landslide risk in the NH 58 area. The
study’s findings are beneficial for policymakers, planners, and other stakeholders involved in regional
disaster risk management. This research offers a comprehensive analysis of landslide-influencing
factors in the NH 58 area and introduces an LSZ map as a valuable tool for managing and mitigating
landslide risks. The map also serves as a critical reference for future research and contributes to the
broader understanding of landslide susceptibility in the region.