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<p>The frequency and magnitude of landslides in the Western Himalayas have escalated in recent years. This significant corridor from Chamoli to Joshimath runs on active tectonic belts like MCT-1 (Munsiari Thrust) and MCT-2 (Vaikrita Thrust), and it faces severe landslide hazards. Therefore, the main objectives of this study are i) to estimate the slope conditions, applying Rock Mass Rating (RMR), Slope Mass Rating (SMR), andLimit Equilibrium Method (LEM) ii) to classify the landslide susceptibility zones using Artificial Neural Network (ANN) and Support Vector Machine (SVM), and iii) to identify the apparent failure patterns of the structurally controlled slopes through Kinematic analysis. The ANN model has more accuracy (AUC = 0.871), and the road section from Pipalkoti to Hailang is highly vulnerable. Results also revealed that most of the investigated slopes (7 out of 10 slopes) are unstable because the SMR value is less than 40, and most of them have a probability of planar or wedge failures. The majority of the rock cum debris slopes are highly vulnerable to sliding as the measured Factor of Safety (FoS) value is less than 1; those are mostly triggered by consistent rainfall and anthropogenic activities. Landslide hotspots are confined to these vulnerable rock types like phyllite, shale, slate, dolomite, gneiss, schist, and quartzite. The entire road section and its adjacent areas experienced large numbers of severe earthquakes (&gt;5 magnitude) within the last three decades. This type of research also provides a sustainable environmental management plan for the socio-economic lifeline of the local people, pilgrims and Indo-Tibetan Border Security.</p>

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Growing risk of landslides and landslide hotspots along the Chamoli-Joshimath corridor in the Garhwal Himalayas

  • 期刊论文

  • 发表日期：

    2025年11月12日

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  • Biswajit Bera

摘要

The frequency and magnitude of landslides in the Western Himalayas have escalated in recent years. This significant corridor from Chamoli to Joshimath runs on active tectonic belts like MCT-1 (Munsiari Thrust) and MCT-2 (Vaikrita Thrust), and it faces severe landslide hazards. Therefore, the main objectives of this study are i) to estimate the slope conditions, applying Rock Mass Rating (RMR), Slope Mass Rating (SMR), andLimit Equilibrium Method (LEM) ii) to classify the landslide susceptibility zones using Artificial Neural Network (ANN) and Support Vector Machine (SVM), and iii) to identify the apparent failure patterns of the structurally controlled slopes through Kinematic analysis. The ANN model has more accuracy (AUC = 0.871), and the road section from Pipalkoti to Hailang is highly vulnerable. Results also revealed that most of the investigated slopes (7 out of 10 slopes) are unstable because the SMR value is less than 40, and most of them have a probability of planar or wedge failures. The majority of the rock cum debris slopes are highly vulnerable to sliding as the measured Factor of Safety (FoS) value is less than 1; those are mostly triggered by consistent rainfall and anthropogenic activities. Landslide hotspots are confined to these vulnerable rock types like phyllite, shale, slate, dolomite, gneiss, schist, and quartzite. The entire road section and its adjacent areas experienced large numbers of severe earthquakes (>5 magnitude) within the last three decades. This type of research also provides a sustainable environmental management plan for the socio-economic lifeline of the local people, pilgrims and Indo-Tibetan Border Security.