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Landslide susceptibility assessment using GIS-based multicriteria decision analysis (MCDA) along a part of National Highway-1, Kashmir- Himalayas, India

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Abstract

The current study aims at GIS-based multicriteria decision analysis to generate a landslide-susceptible map from Baramulla to Uri Road segment along NH-1, Kashmir Himalaya, India. The landslide causative factors examined to generate our AHP matrix are slope gradient, elevation, slope aspect, curvature, distance to drainage, distance to roads, distance to lineaments, geology, land use/land cover, and Rainfall. The study mapped and identified the active landslides along NH-1 through extensive field investigations and other secondary data sources. The landslide events were dominated by rockfall and debris slides. Based on their importance in landslide occurrences, the thematic layers were given relative relevance scores using Saaty's scale. Besides, the Analytic Hierarchy Process was employed to normalize the relative weights and attributes of the various thematic layers. In addition, all thematic data layers were combined using a weighted linear approach to generate the landslide susceptibility map. Furthermore, the resultant landslide susceptibility map was classed into five categories viz., very high (24.18%), high (30.24%), medium (28.61%), low (15.28%), and very low (1.69%). The study reveals that 54.42% of the area falls under the high and very high susceptible zones. Likewise, 78.9% of overall model accuracy of final landslide susceptible zonation map was computed using the area under curve method. Moreover, this study would aid infrastructural, geo-environmental, and landslide hazard planning in the studied region.

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  1. Department of Civil Engineering, National Institute of Technology, Srinagar 190006, India

    Iftikhar Hussain Beigh & Syed Kaiser Bukhari

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Beigh, I.H., Bukhari, S.K. Landslide susceptibility assessment using GIS-based multicriteria decision analysis (MCDA) along a part of National Highway-1, Kashmir- Himalayas, India. Appl Geomat (2024). https://doi.org/10.1007/s12518-024-00559-6

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