ArticleEnvironmental monitoring and assessment2022
GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya.
Article in Environmental monitoring and assessment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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8 citing papers in PubMed.
- GIS-based multi-criteria landslide susceptibility assessment in the Northwestern Himalaya, Jammu and Kashmir, India.Scientific reports · 2026Article
- GIS-based landslide susceptibility zonation using weighted overlay analysis with AHP: a case study of Malappuram district, Kerala, India.Scientific reports · 2026Article
- Integrated analytical hierarchy process and neural network approaches for assessment of soil erosion risk in Manjira River sub-basin, India.Scientific reports · 2026Article
- Landslide susceptibility assessment using hybrid geospatial, frequency ratio, and AHP models in Souk Ahras province, Northeastern of Algeria.Scientific reports · 2025Article
- Performance comparison of post-earthquake disaster susceptibility assessment models based on GIS: a case study of the Lushan County in Ya'an City, China.Scientific reports · 2025Article
- Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Great Xi'an Region, China.Scientific reports · 2024Article
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Corrections and comments
- Erratum issued
Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Landslides are one the most destructive and life-endangering hazard in the Darjeeling Himalayan region and keeping in mind the interest of society and their future prospects identification of landslide potential areas is a very pertinent task in this area. Therefore, the present study aimed toward the landslide susceptibility zonation (LSZ) mapping in and around the Kalimpong region by applying Analytic Hierarchy Process (AHP) method integrated with fifteen causative factors including slope, lineament, drainage density, land use land cover, relative relief, soil texture, lithology, elevation, aspect, thrust and faults, plan curvature, profile curvature, road network, topographic wetness index and stream power index. Tolerance and variance inflation factors with Pearson's correlation coefficient are used to assess potential collinearity among the selected factors, and subsequently, the final model has been constructed by enduring an acceptable consistency ratio (<0.10). Thereafter, to classify this region into very low, low, moderate, high and very high susceptible zones quantile, geometric interval, Jenk's natural break and success rate curve (SRC) techniques are adopted to compare and check the optimum LSZ categorization. Considering the identified 647 landslides, Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curve is used to gauge the best LSZ map. The AUC ROC shows SRC method (m = 0.9) yields the highest result, achieving a prediction accuracy of 79.5% and, therefore, is considered the most promising LSZ form for the present study area. The results obtained from the study highlight the spatial information of areas that may face slope instability and helps government agencies, stakeholders for drafting adequate measures due to absence of proper landslide early warning systems in this region.
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