Abstract

The Kalsi-Chakrata road corridor, located in the Lesser Himalayas, experiences several landslides every year, resulting in a considerable amount of damage to roads, assets, and other infrastructure and even loss of lives. During the monsoon season (June–August), the disruption of routes due to landslide resulted in economic losses and barred villagers from accessing critical and essential facilities, thus impacting the livelihood of the communities residing along the road corridor. Hence immediate requirement was to systematically assess the landslide susceptibility for the study area that would support in the preparation of the planning and mitigation goal, both short and long term. The present study adopted the fuzzy analytic hierarchy process (fuzzy AHP) method integrated with geospatial technology that may be highly effective for landslide susceptibility assessment in the landslide-prone Lesser Himalayas. The use of validated landslide inventory data and high-resolution remote sensing images for selection and mapping of landslide conditioning factors gratifies the geospatial aspect of local variations across the Kalsi-Chakrata road corridor. For fuzzy AHP model setup, prominent landslide contributing factors viz., slope, aspect, altitude, lithology, proximity to road, fault & drainage, Stream Power Index (SPI), Topographical Wetness Index (TWI), rainfall, land use/land cover (LULC), soil, and seismicity, were mapped and classified into significant classes. The resultant landslide susceptibility map (LSM) shows that about 55% (45.23 km2) of the study area was categorized as a very high and high landslide susceptibility zone. Of this, about 21% (17.7 km2) was within very high LSM zone, while 33% (27.5 km2) fell under high landslide susceptibility categories. Approximately 17.6% (14.5 km2) areas fall within the moderately susceptible zone, where chances of future landslides may be amplified without periodic observation and prospective study. At the village level, it was observed that Jhutaya village, located nearer foothill of Lesser Himalayas, is most susceptible to landslide followed by Dhaira, Chapanu, and Sairi villages. The fuzzy AHP model shows 86.52% accuracy in landslide prediction evaluated through the ROC curve (at 95% confidence level). Hence, the output LSI may be referred by the planners and engineers for monitoring, prevention, and mitigation of landslide hazards and development of infrastructure in the Kalsi-Chakrata road corridor, and the fuzzy AHP methodology adopted may be applied in other areas of the Lesser Himalayas.

Highlights

  • In the Himalayan terrain, landslides are among the most common natural hazards that carry a significant threat to the socio-economic development in an area and the life and livelihood of the communities residing in those mountainous regions

  • The geospatial technology integrated with the fuzzy analytic hierarchy process (AHP) based multicriteria decision-making (MCDM) technique focuses more on the correlation of landslide conditioning factors and the spatial distribution of landslide susceptibility

  • To systematically assess the landslide hazard in both the short and long term, this study adopted an integrated geographic information system (GIS)-based fuzzy AHP – MCDM approach to understanding the processes that contribute to landslides landslide-prone Lesser Himalayas

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Summary

Introduction

In the Himalayan terrain, landslides are among the most common natural hazards that carry a significant threat to the socio-economic development in an area and the life and livelihood of the communities residing in those mountainous regions. The statistics published by the Geological Survey of India (GSI) shows that about 0.14 million sq km area in North West Himalayas (Uttarakhand, Himachal Pradesh, and Jammu & Kashmir) are prone to landslide hazard. In addition to rugged terrain, high seismicity, and climatic phenomena that are the main triggering factor for landslide occurrence (Kwan et al 2014; Li et al 2014), the hydro-geological conditions, and anthropogenic activities are among the key elements that too strongly influence the landslide occurrences in the Himalayan region of India (Rai et al 2014; Singh et al 2020)

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