Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools

Landslide is a major threat in many regions with humid climate condition. In recent years, this phenomenon has been accelerated by human activities mainly by rural and urban development projects. This research integrates the GIS tools and multivariate regression analysis for landslide susceptibilit...

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Main Authors:	Fadzil, Mat Yahaya, Akbari, Abolghasem, Azamirad, Mahmoud, Fanodi, Mohsen
Format:	Article
Language:	English English
Published:	Electronic Journal of Geotechnical Engineering 2014
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/5671/ http://umpir.ump.edu.my/id/eprint/5671/ http://umpir.ump.edu.my/id/eprint/5671/1/Fadzil_Bin_Mat_Yahaya.pdf http://umpir.ump.edu.my/id/eprint/5671/3/Landslide%20Susceptibility%20Mapping%20Using%20Logistic%20Regression%20Analysis%20and%20GIS%20Tools.pdf

id	ump-5671
recordtype	eprints
spelling	ump-56712016-01-20T02:15:09Z http://umpir.ump.edu.my/id/eprint/5671/ Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools Fadzil, Mat Yahaya Akbari, Abolghasem Azamirad, Mahmoud Fanodi, Mohsen TA Engineering (General). Civil engineering (General) Landslide is a major threat in many regions with humid climate condition. In recent years, this phenomenon has been accelerated by human activities mainly by rural and urban development projects. This research integrates the GIS tools and multivariate regression analysis for landslide susceptibility modeling (LSM) in north of Iran. To map the landslide susceptibility, ten potential independent variables were selected as effectual factors, including geological formation, terrain elevation, terrain slope and aspect, proximity to roads, proximity to faults and proximity to main rivers, soil unit, land use and annual rainfall. A GIS-database was developed containing all variables for the study area. Previous records of landslides in the study area were mapped based on inventory reports, satellite image processing and field survey using handhold GPS. The slope, proximity to roads, elevation, aspect and soil units was found to be the most effective factors in landslides respectively. Five other factors had no significant effect on landslides in this region. Landslide susceptibility map was then generated based on multivariate regression equation in a raster GIS environment and classified in five susceptibility classes. About 11.16% of the study area has very low susceptibility, 40.36% has low susceptibility, 32.37% has moderate susceptibility, 12.90 % has high susceptibility and 3.23 % has very high susceptibility. Electronic Journal of Geotechnical Engineering 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5671/1/Fadzil_Bin_Mat_Yahaya.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/5671/3/Landslide%20Susceptibility%20Mapping%20Using%20Logistic%20Regression%20Analysis%20and%20GIS%20Tools.pdf Fadzil, Mat Yahaya and Akbari, Abolghasem and Azamirad, Mahmoud and Fanodi, Mohsen (2014) Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools. The Electronic Journal of Geotechnical Engineering (EJGE), 19. pp. 1687-1696. ISSN 1089-3032 http://www.ejge.com/2014/Ppr2014.161mar.pdf
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Fadzil, Mat Yahaya Akbari, Abolghasem Azamirad, Mahmoud Fanodi, Mohsen Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools
description	Landslide is a major threat in many regions with humid climate condition. In recent years, this phenomenon has been accelerated by human activities mainly by rural and urban development projects. This research integrates the GIS tools and multivariate regression analysis for landslide susceptibility modeling (LSM) in north of Iran. To map the landslide susceptibility, ten potential independent variables were selected as effectual factors, including geological formation, terrain elevation, terrain slope and aspect, proximity to roads, proximity to faults and proximity to main rivers, soil unit, land use and annual rainfall. A GIS-database was developed containing all variables for the study area. Previous records of landslides in the study area were mapped based on inventory reports, satellite image processing and field survey using handhold GPS. The slope, proximity to roads, elevation, aspect and soil units was found to be the most effective factors in landslides respectively. Five other factors had no significant effect on landslides in this region. Landslide susceptibility map was then generated based on multivariate regression equation in a raster GIS environment and classified in five susceptibility classes. About 11.16% of the study area has very low susceptibility, 40.36% has low susceptibility, 32.37% has moderate susceptibility, 12.90 % has high susceptibility and 3.23 % has very high susceptibility.
format	Article
author	Fadzil, Mat Yahaya Akbari, Abolghasem Azamirad, Mahmoud Fanodi, Mohsen
author_facet	Fadzil, Mat Yahaya Akbari, Abolghasem Azamirad, Mahmoud Fanodi, Mohsen
author_sort	Fadzil, Mat Yahaya
title	Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools
title_short	Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools
title_full	Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools
title_fullStr	Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools
title_full_unstemmed	Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools
title_sort	landslide susceptibility mapping using logistic regression analysis and gis tools
publisher	Electronic Journal of Geotechnical Engineering
publishDate	2014
url	http://umpir.ump.edu.my/id/eprint/5671/ http://umpir.ump.edu.my/id/eprint/5671/ http://umpir.ump.edu.my/id/eprint/5671/1/Fadzil_Bin_Mat_Yahaya.pdf http://umpir.ump.edu.my/id/eprint/5671/3/Landslide%20Susceptibility%20Mapping%20Using%20Logistic%20Regression%20Analysis%20and%20GIS%20Tools.pdf
first_indexed	2023-09-18T22:01:02Z
last_indexed	2023-09-18T22:01:02Z
_version_	1777414400119930880

Landslide Susceptibility Mapping Using Logistic Regression Analysis and GIS Tools

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