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

• Published in: Conservation Vol. 3; no. 3; pp. 444 - 459
• Main Authors: Sharma, Mukta, Upadhyay, Ritambhara K., Tripathi, Gaurav, Kishore, Naval, Shakya, Achala, Meraj, Gowhar, Kanga, Shruti, Singh, Suraj Kumar, Kumar, Pankaj, Johnson, Brian Alan, Thakur, Som Nath
• Format: Journal Article
• Language: English
• Published: Ciudad Real MDPI AG 01.09.2023
• Subjects: Climate change; Discriminant analysis; Failure; Fuzzy logic; Geology; GIS; Infrastructure; landslide; landslide susceptibility zonation; Landslides & mudslides; logistic regression; LULC; Machine learning; Regression analysis; Remote sensing; Roads & highways; Seasons; Statistical methods; Stone; Wind; Himalaya Mountains; Uttarakhand India; India
• ISSN: 2673-7159; 2673-7159
• DOI: 10.3390/conservation3030030

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.