National-scale landslide susceptibility and risk mapping of India using a hybrid data-driven approach

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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Publication Details

Journal
Scientific Reports
Published
2025-12-31
DOI
https://doi.org/10.1038/s41598-025-33446-0
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
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article

National-scale landslide susceptibility and risk mapping of India using a hybrid data-driven approach

Md. Sarfaraz Asgher, Harish Bahuguna, Imran Khan, Ashutosh Kainthola et al.
Scientific Reports
Landslides and related hazards
article

National-scale landslide susceptibility and risk mapping of India using a hybrid data-driven approach

Md. Sarfaraz Asgher, Harish Bahuguna, Imran Khan, Ashutosh Kainthola, Rayees Ahmed
article en

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.

Scientific ReportsVol. 16(1)
University of Jammu (IN), Indian Institute of Science Bangalore (IN), Geological Survey of India (IN), Banaras Hindu University (IN)
Climate action
Openalex Percentile: Top 25%
Landslides and related hazards
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