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<p>Landslides pose a serious threat to humans and the environment, involving the sliding of rock, soil, and debris down slopes. Pulwama district in Jammu &amp; Kashmir, India, faces considerable risks due to its rugged topography, heavy rainfall, and various human activities. Creating a landslide susceptibility map is crucial for preventing, predicting, and reducing these disasters. This study presents the first comprehensive and validated attempt to generate a landslide susceptibility map for the Pulwama district using a geospatial multi-criteria decision-making approach. It employs Multi-Criteria Decision Making and the Analytical Hierarchical Process (AHP) within a Geographic Information System (GIS), relying entirely on secondary data sources, to identify landslide susceptibility zones in Pulwama, Jammu &amp; Kashmir. Different causative factors, such as elevation, slope, lithology, rainfall, soil, stratigraphy, geomorphology, distance from lineaments, distance from streams, and vegetation cover, are assessed through pairwise comparisons using AHP. Weighted Overlay Analysis, a spatial analysis method, has been applied in ArcGIS Pro to determine weights and is validated by calculating the consistency ratio. The susceptibility map classifies the region into five categories: very low (129.61 sq. km, 14.55%), low (247.57 sq. km, 27.78%), moderate (230.81 sq. km, 25.90%), high (171.63 sq. km, 19.26%), and very high (111.45 sq. km, 12.51%) susceptibility. The northern and north-eastern areas are identified as the most vulnerable. For validation, a total of 74 historical landslide incidents from 2016 to 2024 have been located using Google Earth Pro’s historical imagery tool and various news reports and articles. These locations serve as ground truth labels to assess the accuracy of the generated landslide susceptibility map. An ROC—AUC (Receiver Operating Characteristic–Area Under Curve) of 0.934 and an average precision score of 0.896 demonstrate the effectiveness of combining GIS with AHP for landslide susceptibility mapping. Implementing strict mitigation measures, proper land use regulations, slope stabilisation techniques, and afforestation is essential to minimize the impacts of landslides.</p>

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GIS-Based Multi-Criteria Analysis for Landslide Susceptibility Mapping in Pulwama, Jammu and Kashmir

  • 期刊论文

  • 发表日期：

    2025年8月21日

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  • Mahalingam Bose,
  • Indrakant Behera,
  • B. C. Vishwanath

摘要

Landslides pose a serious threat to humans and the environment, involving the sliding of rock, soil, and debris down slopes. Pulwama district in Jammu & Kashmir, India, faces considerable risks due to its rugged topography, heavy rainfall, and various human activities. Creating a landslide susceptibility map is crucial for preventing, predicting, and reducing these disasters. This study presents the first comprehensive and validated attempt to generate a landslide susceptibility map for the Pulwama district using a geospatial multi-criteria decision-making approach. It employs Multi-Criteria Decision Making and the Analytical Hierarchical Process (AHP) within a Geographic Information System (GIS), relying entirely on secondary data sources, to identify landslide susceptibility zones in Pulwama, Jammu & Kashmir. Different causative factors, such as elevation, slope, lithology, rainfall, soil, stratigraphy, geomorphology, distance from lineaments, distance from streams, and vegetation cover, are assessed through pairwise comparisons using AHP. Weighted Overlay Analysis, a spatial analysis method, has been applied in ArcGIS Pro to determine weights and is validated by calculating the consistency ratio. The susceptibility map classifies the region into five categories: very low (129.61 sq. km, 14.55%), low (247.57 sq. km, 27.78%), moderate (230.81 sq. km, 25.90%), high (171.63 sq. km, 19.26%), and very high (111.45 sq. km, 12.51%) susceptibility. The northern and north-eastern areas are identified as the most vulnerable. For validation, a total of 74 historical landslide incidents from 2016 to 2024 have been located using Google Earth Pro’s historical imagery tool and various news reports and articles. These locations serve as ground truth labels to assess the accuracy of the generated landslide susceptibility map. An ROC—AUC (Receiver Operating Characteristic–Area Under Curve) of 0.934 and an average precision score of 0.896 demonstrate the effectiveness of combining GIS with AHP for landslide susceptibility mapping. Implementing strict mitigation measures, proper land use regulations, slope stabilisation techniques, and afforestation is essential to minimize the impacts of landslides.