Abstract Landslides initiate when a geophysical mass, including rocks, mud, or debris, gets dislodged due to gravitational force, often triggered by various factors like rainfall, earthquakes, or anthropogenic activities. These events are particularly dangerous in mountainous areas, where they can cause significant harm to both human life and infrastructure. Detection of landslides is essential not only for diminishing damage but also for developing effective disaster management strategies. In this study, landslide susceptibility mapping was carried out in parts of the Kailash Mansarovar Pilgrimage road using multi-criteria decision-making methods like the Analytical Hierarchy Process (AHP) and the AHP-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A landslide inventory was initially created by visual analysis of images sourced from Google Earth Pro, which resulted in the mapping of 100 landslide occurrences in 2024 along the road. Fourteen of the most important landslide causative/conditioning parameters, including Slope, Aspect, Stream Power Index (SPI), Drainage Density (DD), Lithology, Geomorphology, Distance from Lineament, Distance from Fault, Sediment Transport Index (STI), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Land Use/Land Cover (LULC), were utilized as inputs in this study. The landslide susceptibility maps, thus generated using both the AHP-TOPSIS and AHP methods, indicated an increasing likelihood of landslides in the study area, thereby increasing knowledge and helping in disaster risk reduction. The AHP-TOPSIS method achieved a precision of 83.10%, while the AHP method achieved a ROC/AUC score of 76.30%. These findings provide valuable insights for planners, managers, and decision-makers in developing effective landslide risk management strategies along this critical route. Research highlights <UnorderedList Mark="Bullet"> <ItemContent> High-accuracy susceptibility mapping using AHP and AHP-TOPSIS </ItemContent> <ItemContent> Weak lithology, steep slopes, and human activities control landslide risk </ItemContent> <ItemContent> Outputs support disaster mitigation and infrastructure planning </ItemContent> </UnorderedList>

Earth observation based landslide susceptibility mapping along the Kailash-Mansarovar route in Uttarakhand Himalaya using AHP-TOPSIS integrated approach

期刊论文
发表日期：

2026年5月4日

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Dipankar Das,
Shovan L Chattoraj,
S Aditya Narayanan

摘要

Abstract

Landslides initiate when a geophysical mass, including rocks, mud, or debris, gets dislodged due to gravitational force, often triggered by various factors like rainfall, earthquakes, or anthropogenic activities. These events are particularly dangerous in mountainous areas, where they can cause significant harm to both human life and infrastructure. Detection of landslides is essential not only for diminishing damage but also for developing effective disaster management strategies. In this study, landslide susceptibility mapping was carried out in parts of the Kailash Mansarovar Pilgrimage road using multi-criteria decision-making methods like the Analytical Hierarchy Process (AHP) and the AHP-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A landslide inventory was initially created by visual analysis of images sourced from Google Earth Pro, which resulted in the mapping of 100 landslide occurrences in 2024 along the road. Fourteen of the most important landslide causative/conditioning parameters, including Slope, Aspect, Stream Power Index (SPI), Drainage Density (DD), Lithology, Geomorphology, Distance from Lineament, Distance from Fault, Sediment Transport Index (STI), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Land Use/Land Cover (LULC), were utilized as inputs in this study. The landslide susceptibility maps, thus generated using both the AHP-TOPSIS and AHP methods, indicated an increasing likelihood of landslides in the study area, thereby increasing knowledge and helping in disaster risk reduction. The AHP-TOPSIS method achieved a precision of 83.10%, while the AHP method achieved a ROC/AUC score of 76.30%. These findings provide valuable insights for planners, managers, and decision-makers in developing effective landslide risk management strategies along this critical route.

Research highlights

High-accuracy susceptibility mapping using AHP and AHP-TOPSIS

Weak lithology, steep slopes, and human activities control landslide risk

Outputs support disaster mitigation and infrastructure planning