ArticleScientific reports2025
National-scale landslide susceptibility and risk mapping of India using a hybrid data-driven approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Epigenetic memory in periodontal healing: mechanisms, evidence, and emerging therapeutic perspectives.Odontology · 2026Review
- Geological and machine learning based landslide risk assessment in the context of community knowledge and perception.Scientific reports · 2026Article
- Detoxification of organic pollutants over novel niobium and cobalt codoped molybdenum oxy-sulfide photocatalyst under visible light.Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology · 2026Article
- Field applications of sewage sludge improved soil properties and increased wheat yield in sand-textured soils of hot drylands.Biodegradation · 2026Article
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5 authors.
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Abstract
Landslides pose a persistent and deadly hazard across India's diverse terrain, demanding robust, high-resolution national-scale assessments to guide effective risk mitigation and policy decisions. This study addresses a critical research gap in landslide susceptibility and risk assessment across India at a high spatial resolution of 90 m × 90 m on a national scale. Leveraging a robust landslide inventory comprising 109,504 documented events and incorporating causative factors selected through expert judgment, three models Analytic Hierarchy Process (AHP), Frequency Ratio (FR), and Yule's Coefficient (Yc) were employed to reduce model bias and identify the most reliable susceptibility and risk mapping approach through comprehensive field validation and map interpretation. The results indicate, very high landslide susceptibility zones cover 4.0% (AHP), 4.2% (Yc), and 4.5% (FR) of India's land area, while combined high and very high susceptibility classes account for 11.0% (AHP), 10.4% (Yc), and 10.5% (FR). Model evaluation demonstrates robust predictive performance, with accuracies of 89.1% (AHP), 88.9% (Yc), and 90.5% (FR) according to the ROC curve, and 86.7%, 85.6%, and 87.2% respectively via the precision-recall curve. Minor variations in susceptibility extent and accuracy among models underscore the resilience of the input data, however the Yc model provides a a more ground representative output based on factor weighting. Utilizing the Yc model and exposure data, approximately 8,606 km² (0.26% of India) are classified as having a high to extremely high landslide risk at a qualitative scale. Overall, the susceptibility analysis indicates that approximately 0.34 million km² is categorized as having high and very high susceptibility under Yc model. The analysis identifies Nagaland (55%), Mizoram (53.1%), Arunachal Pradesh (52.1%), Sikkim (45.0%), Uttarakhand (40.0%), Manipur (30.6%), Himachal Pradesh (28.9%), Meghalaya (22.0%), Jammu & Kashmir (20.2%), and Tripura (8.0%) as the ten most landslide-susceptible States and Union Territories , listed in descending order of susceptibility. These findings provide critical insights for disaster risk reduction strategies and policymaking, enhancing landslide prediction, mitigation, and resilience planning across susceptible regions at mapped scale.
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