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<p>The young and dynamic Himalaya region of Uttarakhand is prone to frequent landslides due to its complex and geologically active characteristics. Slope stability is highly affected by the rapid development of infrastructure and changing climate scenario, causing significant landslide events in this region. Therefore, for better planning and risk management, it is crucial to assess landslide susceptibility. In this study, spatial analysis of landslide susceptibility maps (LSMs) produced along a road corridor from Rishikesh to Srinagar on National Highway-7 (NH-7) using the frequency ratio (FR), analytical hierarchy process (AHP), and information value method (IVM) in Geographic Information System (GIS) framework was done. Depending upon the physiography of the area and expert opinion various conditioning factors were considered, including elevation, slope, aspect, curvature, lithology, rainfall, Topographic Roughness Index (TRI), Topographic wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI) and lineament density responsible for landslide occurrences. A multicollinearity assessment was performed to ensure the independence of selected landslide-causing factors. The accuracy of the methods used was evaluated using the Receiver Operating Characteristic (ROC) curve. The results indicate that AHP provided an accuracy of 81.9% followed by FR (78.3%) and IVM (75.2%) in producing LSMs. The resulting maps were classified into five landslide-prone zones from very low to very high categories. The results show that, LSMs produced through AHP offer valuable information which can be used by decision-makers for risk reduction and resilience in the community. Additionally, this study demonstrated the ease and effectiveness of the methods used in this work for landslide susceptibility mapping in other similar geographical regions.</p>

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Evaluating landslide susceptibility along a road corridor in complex Himalayan Terrain, India using analytical hierarchy process, frequency ratio and information value methods

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Shreyans Agarwal,
  • Manish Pandey,
  • Maya Kumari,
  • Akshay Kumar,
  • Shubham Badola,
  • Varun Narayan Mishra,
  • Mohamed Zhran

摘要

The young and dynamic Himalaya region of Uttarakhand is prone to frequent landslides due to its complex and geologically active characteristics. Slope stability is highly affected by the rapid development of infrastructure and changing climate scenario, causing significant landslide events in this region. Therefore, for better planning and risk management, it is crucial to assess landslide susceptibility. In this study, spatial analysis of landslide susceptibility maps (LSMs) produced along a road corridor from Rishikesh to Srinagar on National Highway-7 (NH-7) using the frequency ratio (FR), analytical hierarchy process (AHP), and information value method (IVM) in Geographic Information System (GIS) framework was done. Depending upon the physiography of the area and expert opinion various conditioning factors were considered, including elevation, slope, aspect, curvature, lithology, rainfall, Topographic Roughness Index (TRI), Topographic wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI) and lineament density responsible for landslide occurrences. A multicollinearity assessment was performed to ensure the independence of selected landslide-causing factors. The accuracy of the methods used was evaluated using the Receiver Operating Characteristic (ROC) curve. The results indicate that AHP provided an accuracy of 81.9% followed by FR (78.3%) and IVM (75.2%) in producing LSMs. The resulting maps were classified into five landslide-prone zones from very low to very high categories. The results show that, LSMs produced through AHP offer valuable information which can be used by decision-makers for risk reduction and resilience in the community. Additionally, this study demonstrated the ease and effectiveness of the methods used in this work for landslide susceptibility mapping in other similar geographical regions.