Earth observation data and GIS based landslide susceptibility analysis through frequency ratio model in lesser Himalayan region, India

Landslide incidents are resulted into significant monetary losses, human deaths, and irrevocable changes to the natural landscape. Basically, geological, climatic, and human factors contribute to landslides. In this research, the landslide susceptibility mapping was applied using satellite data and...

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Main Authors: Sheetal Bisht, Kishan Singh Rawat, Sudhir Kumar Singh
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:Quaternary Science Advances
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2666033423000734
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author Sheetal Bisht
Kishan Singh Rawat
Sudhir Kumar Singh
author_facet Sheetal Bisht
Kishan Singh Rawat
Sudhir Kumar Singh
author_sort Sheetal Bisht
collection DOAJ
description Landslide incidents are resulted into significant monetary losses, human deaths, and irrevocable changes to the natural landscape. Basically, geological, climatic, and human factors contribute to landslides. In this research, the landslide susceptibility mapping was applied using satellite data and a probability-frequency ratio model with overlay analysis for the lesser Himalayan region in GIS. For this purpose, ten factors that affect the likelihood of landslides have been taken into account. The topographical data analysis provides information on parameters like slope, curvature, aspect, and distance from drainage. The Giovanni website's rainfall database was used to calculate the quantity of precipitation, and land use/land cover (LULC) of ESRI was used for susceptibility analysis.The weight of each factor was determined using Frequency ratio model and afterwards the overlay was performed using GIS. Landslide inventory was downloaded from Bhukosh-Geological Survey of India portal. With the aid of landslide spot data, the results of the inspection were verified and susceptibility map was categorised in different class. The accuracy of the susceptibility map was 73.2%. Landslide susceptibility map (LSM) was classified into four classes namely non-hazardous zone, moderately hazardous zone, highly hazardous zone, and very hazardous zone. This model's data can be used to estimate the likelihood of hazards to local residents, the environment, and any existing foundational structures.
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spelling doaj.art-615827d68dbb472a8c29ebf51aa6cc8c2024-03-08T05:19:25ZengElsevierQuaternary Science Advances2666-03342024-01-0113100141Earth observation data and GIS based landslide susceptibility analysis through frequency ratio model in lesser Himalayan region, IndiaSheetal Bisht0Kishan Singh Rawat1Sudhir Kumar Singh2Civil Engineering Department, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, IndiaCivil Engineering Department, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India; Corresponding author.K. Banerjee Centre of Atmospheric & Ocean Studies (KBCAOS), IIDS, Nehru Science Centre, University of Allahabad, Prayagraj, Uttar Pradesh, IndiaLandslide incidents are resulted into significant monetary losses, human deaths, and irrevocable changes to the natural landscape. Basically, geological, climatic, and human factors contribute to landslides. In this research, the landslide susceptibility mapping was applied using satellite data and a probability-frequency ratio model with overlay analysis for the lesser Himalayan region in GIS. For this purpose, ten factors that affect the likelihood of landslides have been taken into account. The topographical data analysis provides information on parameters like slope, curvature, aspect, and distance from drainage. The Giovanni website's rainfall database was used to calculate the quantity of precipitation, and land use/land cover (LULC) of ESRI was used for susceptibility analysis.The weight of each factor was determined using Frequency ratio model and afterwards the overlay was performed using GIS. Landslide inventory was downloaded from Bhukosh-Geological Survey of India portal. With the aid of landslide spot data, the results of the inspection were verified and susceptibility map was categorised in different class. The accuracy of the susceptibility map was 73.2%. Landslide susceptibility map (LSM) was classified into four classes namely non-hazardous zone, moderately hazardous zone, highly hazardous zone, and very hazardous zone. This model's data can be used to estimate the likelihood of hazards to local residents, the environment, and any existing foundational structures.http://www.sciencedirect.com/science/article/pii/S2666033423000734Landslide susceptibilityFrequency ratio modelOverlay analysisRudraprayagThematic layers
spellingShingle Sheetal Bisht
Kishan Singh Rawat
Sudhir Kumar Singh
Earth observation data and GIS based landslide susceptibility analysis through frequency ratio model in lesser Himalayan region, India
Quaternary Science Advances
Landslide susceptibility
Frequency ratio model
Overlay analysis
Rudraprayag
Thematic layers
title Earth observation data and GIS based landslide susceptibility analysis through frequency ratio model in lesser Himalayan region, India
title_full Earth observation data and GIS based landslide susceptibility analysis through frequency ratio model in lesser Himalayan region, India
title_fullStr Earth observation data and GIS based landslide susceptibility analysis through frequency ratio model in lesser Himalayan region, India
title_full_unstemmed Earth observation data and GIS based landslide susceptibility analysis through frequency ratio model in lesser Himalayan region, India
title_short Earth observation data and GIS based landslide susceptibility analysis through frequency ratio model in lesser Himalayan region, India
title_sort earth observation data and gis based landslide susceptibility analysis through frequency ratio model in lesser himalayan region india
topic Landslide susceptibility
Frequency ratio model
Overlay analysis
Rudraprayag
Thematic layers
url http://www.sciencedirect.com/science/article/pii/S2666033423000734
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AT sudhirkumarsingh earthobservationdataandgisbasedlandslidesusceptibilityanalysisthroughfrequencyratiomodelinlesserhimalayanregionindia