Landslide susceptibility mapping using frequency ratio method along the Bhaderwah-Bani Road, Jammu and Kashmir, India

Yudhbir Singh; Mehreen Liaqat; Sumit Johar; Shifali Chib; Gaurav Singh Parihar

doi:10.51710/jias.v41iI.325

Authors

Yudhbir Singh Department of Geology, University of Jammu, India
Mehreen Liaqat Department of Geography, Jamia Millia Islamia, Delhi
Sumit Johar PG Department of Geology, University of Jammu
Shifali Chib PG Department of Geology, University of Jammu
Gaurav Singh Parihar PG Department of Geology, University of Jammu

DOI:

https://doi.org/10.51710/jias.v41iI.325

Keywords:

: Landslide, Hazard zonation, NH-244, frequency ratio, Remote Sensing, GIS

Abstract

This study outlines a method for landslide susceptibility mapping utilizing remote sensing and geographic information systems (GIS). Since the terrain from Bhaderwah to Bani in J&K is prone to landslide threats, the study has been conducted there. The purpose of the current study is to determine and identify the significant terrain elements causing landslides. Nine contributing factors on landslides were taken into account throughout the analysis. Thematic data layers are created in the GIS domain based on local events. Topographic maps, satellite images, on-site observations and publicly available published maps are used to gather the landscape data. To determine the standardized scores of criteria expressing their factor of importance for a given decision problem in terms of thematic parameters, categories, and their normalized weights the Frequency ratio (FR) method is used to digitize these maps along with tabular data and to create a GIS database. A particular landslide susceptibility zonation (LSZ) map was created on a GIS platform by statistically combining weightages from several thematic maps. Using FR, the LSZ map was divided into five separate susceptibility zones. According to the study's finding, 8% of the area is in a very high susceptibility zone, with the remaining 12%, 15%, 23%, and 42% falling into high, moderate, low, and very low susceptibility zones, respectively. The outcomes of the current study might aid policy and decision makers in moving forward with regional development initiatives including land use planning.

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