Mapping infrastructure vulnerability to landslides in India using high-resolution geospatial data

Ria Joshi, Manabendra Saharia (), Ishita Afreen Ahmed, Nirdesh Sharma and G. V. Ramana
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Ria Joshi: Indian Institute of Technology Delhi
Manabendra Saharia: Indian Institute of Technology Delhi
Ishita Afreen Ahmed: Indian Institute of Technology Delhi
Nirdesh Sharma: Indian Institute of Technology Delhi
G. V. Ramana: Indian Institute of Technology Delhi

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 10, No 16, 11663-11693

Abstract: Abstract Landslides cause significant human and economic losses worldwide, with India accounting for approximately 8% of global fatalities due to landslides. Despite this severe impact, there has been no comprehensive national-scale assessment of infrastructure vulnerability to landslides in India, primarily due to the lack of high-resolution ground data. This study addresses this gap by developing vulnerability indices and maps for landslide-prone infrastructure across India, utilizing extensive ground and satellite-based geospatial datasets. We integrated a national landslide susceptibility map, which categorizes landslides into five classes, with detailed infrastructure density maps for roads, railways, and buildings. Our findings show that approximately 9.13% of roads in India fall into the ‘very high’ vulnerability category, predominantly in the Himalayan and Western Ghats regions. Similarly, 1.37% of the railway network and 3.25% of buildings are classified as very highly vulnerable, with significant risks concentrated in the northeastern states and hilly areas. A Composite Landslide Infrastructure Vulnerability Index (CLIVI) was developed using Principal Component Analysis (PCA) to synthesize infrastructure vulnerabilities into a single metric, capturing the combined susceptibility of critical infrastructure components within each region. Receiver Operating Characteristic (ROC) curve analysis was performed to evaluate the predictive capability of the developed CLIVI, achieving an Area Under the Curve (AUC) score of 0.87, indicating high model performance in distinguishing vulnerable and non-vulnerable infrastructure regions. The results emphasize the urgent need for strategic planning and investment in infrastructure resilience to mitigate the adverse effects of landslides. High-resolution maps and indices generated in this study can inform policymakers and planners, enabling them to prioritize areas for intervention and resource allocation.

Keywords: Landslides; Infrastructure vulnerability; Geospatial datasets; India; Infrastructure resilience (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07256-6

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