Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach

Anik Saha () and Sunil Saha ()
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Anik Saha: University of Gour Banga
Sunil Saha: University of Gour Banga

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 3, No 74, 4453-4483

Abstract: Abstract Kurseong and its surrounding areas are frequently affected by the landslide which causes huge loss of properties and lives. The landslide susceptibility maps can play an important role in human development and sustainable environment management in Darjeeling Himalayan. The present research focused on preparing the effective landslide susceptible models using different statistical probabilistic methods, namely landslide nominal susceptibility factor (LNSF), information value (InfoVal) and certainty factor (CF) models. Experiments have been carried out in Kurseong region, a part of Darjeeling Himalaya as a field of this research. Landslide sites were identified from previous records through extensive field survey and Google pro satellite imagery. Totally, 273 landslide sites were compiled and prepared the inventory map. Out of 273 landslides, 70% were used for training and 30% were used for validating the models. Seventeen landslide conditioning factors were selected for modeling landslide susceptibility, i.e., elevation, aspect, slope degree, rainfall, geological structure, geomorphologic division, lineament, land use/land cover, distance to roads, earthquake zone, soil texture, soil depth, normalized difference vegetation index, drainage density, stream power index and topographic wetness index. The produced susceptibility maps were validated using the receiver’s operating characteristic (ROC) curves. The prediction accuracy of LNSF, InfoVal and CF models as per ROC are 80.78%, 82.91% and 86.13%, respectively. The CF achieved the highest accuracy (86.13%), while the LNSF produced the lowest ROC value (80.78%). However, the comparison of the produced landslide maps revealed that all the applied models have good precision for studying susceptibility of landslide in Kurseong and its surrounding of Darjeeling Himalaya, India. The findings of current study can be supportive for the mitigation of landslide risk in the Kurseong range as well as the surrounding comparable areas having same geoenvironmental conditions.

Keywords: Landslide susceptibility model; Kurseong range; Landslide nominal susceptibility factor; Information value; Certainty factor (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-020-00783-1

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