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Landslide susceptibility mapping using morphological and hydrological parameters in Sikkim Himalaya: frequency ratio model and geospatial technologies

Author

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  • Irjesh Sonker

    (University of Allahabad)

  • Jayant Nath Tripathi

    (University of Allahabad)

  • Swarnim

    (University of Allahabad)

Abstract

Sikkim Himalaya, a part of the North-Eastern Himalayan region, is affected by the landslides and it causes the loss of life, property, and other human infrastructure, etc. The objective of study is identification of landslides susceptibility zones of the Sikkim Himalaya, using various factors/thematic layers, such as absolute relief, relative relief, relief ratio, dissection index, hypsometric integral, slope index, drainage density, drainage frequency, drainage intensity, drainage texture, infiltration number, junction frequency, length of overland flow, ruggedness index, stream transport index, topographic wetness index, stream power index, and rainfall and all these layers are integrated in Arc GIS software using FR model. These spatial factors are generated using Alos Palsar DEM and rainfall data with the help of the Arc GIS. The FR model was utilised for the purpose of determining the weights of such all-thematic layers for the possibility of landslides occurring in regions that are susceptible to the effects of landslides. These weight of such all thematic layers are combined using the Arc GIS to create the map of landslide susceptibility zones. The map of the landslide susceptibility zones of the region has been split into five distinct categories, including ‘very high’ (13.20%), ‘high’ (19.75%), ‘moderate’ (30.81%), ‘low’ (27.14%), ‘very low’ (9.09%). For accuracy analysis of the model the area under the curve is used and is estimated as 84.6% with the help of the FR model and occurrence of previous landslides in the region.

Suggested Citation

  • Irjesh Sonker & Jayant Nath Tripathi & Swarnim, 2024. "Landslide susceptibility mapping using morphological and hydrological parameters in Sikkim Himalaya: frequency ratio model and geospatial technologies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(7), pages 6797-6832, May.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:7:d:10.1007_s11069-024-06491-7
    DOI: 10.1007/s11069-024-06491-7
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    References listed on IDEAS

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    1. Jie Dou & Hiromitsu Yamagishi & Hamid Pourghasemi & Ali Yunus & Xuan Song & Yueren Xu & Zhongfan Zhu, 2015. "An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(3), pages 1749-1776, September.
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