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<p>Mandi district in Himachal Pradesh, located in the seismically active Western Himalayas, is highly landslide-prone due to complex geology, steep terrain, and human pressures. Using GIS, RS, and AHP, a landslide susceptibility zone (LSZ) map was developed based on eight geo-environmental parameters. Lineament density (18.85%), soil type (17.8%), and slope (15.97%) emerged as the most influential factors. The map identifies 9.12% of the area as high-risk, 67.55% as moderate-risk, and 23.33% as low-risk, with an AUC-based validation accuracy of 73% using 918 historical landslides and Google Earth imagery. Rainfall is the dominant trigger (88.32%), followed by anthropogenic activities (10.41%) such as slope cutting and settlement expansion. LULC analysis shows built-up (11.77%) and rangeland (32.82%) as highly susceptible, while vegetation cover (52.38%) corresponds with low risk. Incorporating NGT guidelines, a 100 m buffer around water bodies&#xa0;reveals that 0.24% of built-up land and 96 infrastructure facilities fall in high-risk zones, demanding urgent policy measures. The socio-economic assessment further indicates that high-risk zones overlap with dense populations. Overall, defining hazard zones provides vital inputs for sustainable land-use planning, disaster mitigation, and early warning systems in mountain regions.</p>

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Integrating AHP Decision-Making Tool for Landslide Susceptibility Zone Mapping: Insights from Mandi, Himachal Pradesh

  • 期刊论文

  • 发表日期：

    2025年10月23日

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  • Payal Mahato,
  • Supriya Srivastava,
  • Sadanand Pandey

摘要

Mandi district in Himachal Pradesh, located in the seismically active Western Himalayas, is highly landslide-prone due to complex geology, steep terrain, and human pressures. Using GIS, RS, and AHP, a landslide susceptibility zone (LSZ) map was developed based on eight geo-environmental parameters. Lineament density (18.85%), soil type (17.8%), and slope (15.97%) emerged as the most influential factors. The map identifies 9.12% of the area as high-risk, 67.55% as moderate-risk, and 23.33% as low-risk, with an AUC-based validation accuracy of 73% using 918 historical landslides and Google Earth imagery. Rainfall is the dominant trigger (88.32%), followed by anthropogenic activities (10.41%) such as slope cutting and settlement expansion. LULC analysis shows built-up (11.77%) and rangeland (32.82%) as highly susceptible, while vegetation cover (52.38%) corresponds with low risk. Incorporating NGT guidelines, a 100 m buffer around water bodies reveals that 0.24% of built-up land and 96 infrastructure facilities fall in high-risk zones, demanding urgent policy measures. The socio-economic assessment further indicates that high-risk zones overlap with dense populations. Overall, defining hazard zones provides vital inputs for sustainable land-use planning, disaster mitigation, and early warning systems in mountain regions.