An ensemble model for landslide susceptibility mapping in a forested area

Arabameri, A; Pradhan, B; Rezaei, K; Lee, S; Sohrabi, M

An ensemble model for landslide susceptibility mapping in a forested area

Arabameri, A Pradhan, B

Rezaei, K Lee, S Sohrabi, M

Permalink

Publication Type:: Journal Article
Citation:: Geocarto International, 2019
Issue Date:: 2019-01-01

Closed Access

	Filename	Description	Size
	_system_appendPDF_proof_hi (24).pdf	Submitted Version	3.96 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Arabameri, A	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Rezaei, K	en_US
dc.contributor.author	Lee, S	en_US
dc.contributor.author	Sohrabi, M	en_US
dc.date.accessioned	2020-04-10T06:21:50Z
dc.date.available	2020-04-10T06:21:50Z
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Geocarto International, 2019	en_US
dc.identifier.issn	1010-6049	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139940
dc.description.abstract	© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This article proposes a new methodological approach using a combination of expert knowledge-based (analytic hierarchy process, AHP), bivariate (statistical index, SI) and multivariate (linear discriminant analysis, LDA) models for landslide susceptibility mapping (LSM) in Mazandran Province, Iran. Tolerance and variance inflation factor indicators were used for assessing multi-collinearity among parameters, and three (i.e. profile curvature, soil type and topography wetness index) of 18 factors were eliminated because of multi-collinearity issues. Fifteen geo-environmental conditioning factors including elevation, slope degree, slope aspect, plan curvature, slope length, convergence index, stream power index, distance from river, drainage density, distance from road, distance from fault, lithology, rainfall, land use/landcover and normalized difference vegetation index and 321 landslide locations (testing data set, 70% of total landslides) were used for modeling. The importance of factors showed that distance to road (AHP = 0.201, LDA = 0.301) was the most important factor in landslide occurrence. The validation results using validation data set (138 landslide locations, 30% of total landslides) and area under the receiver operating characteristic curve (AUROC) showed that the ensemble models AHP-LDR (83%), AHP-SI (95%) and SI-LDR (83%) had higher prediction accuracies than the individual AHP (82%), SI (82%) and LDA (79%) models and combination of AHP and SI models along with ALOS-PALSAR remote sensing data and geographic information system (GIS) technique provide powerful tool in LSM in the study area. The results of proposed novel methodological framework can be used by decision-makers and forest engineers for forest management spatially forest roads conservation that have key importance in sustainable development in local and regional scales.	en_US
dc.relation.ispartof	Geocarto International	en_US
dc.relation.isbasedon	10.1080/10106049.2019.1585484	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Geological & Geomatics Engineering	en_US
dc.title	An ensemble model for landslide susceptibility mapping in a forested area	en_US
dc.type	Journal Article
utslib.for	0909 Geomatic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This article proposes a new methodological approach using a combination of expert knowledge-based (analytic hierarchy process, AHP), bivariate (statistical index, SI) and multivariate (linear discriminant analysis, LDA) models for landslide susceptibility mapping (LSM) in Mazandran Province, Iran. Tolerance and variance inflation factor indicators were used for assessing multi-collinearity among parameters, and three (i.e. profile curvature, soil type and topography wetness index) of 18 factors were eliminated because of multi-collinearity issues. Fifteen geo-environmental conditioning factors including elevation, slope degree, slope aspect, plan curvature, slope length, convergence index, stream power index, distance from river, drainage density, distance from road, distance from fault, lithology, rainfall, land use/landcover and normalized difference vegetation index and 321 landslide locations (testing data set, 70% of total landslides) were used for modeling. The importance of factors showed that distance to road (AHP = 0.201, LDA = 0.301) was the most important factor in landslide occurrence. The validation results using validation data set (138 landslide locations, 30% of total landslides) and area under the receiver operating characteristic curve (AUROC) showed that the ensemble models AHP-LDR (83%), AHP-SI (95%) and SI-LDR (83%) had higher prediction accuracies than the individual AHP (82%), SI (82%) and LDA (79%) models and combination of AHP and SI models along with ALOS-PALSAR remote sensing data and geographic information system (GIS) technique provide powerful tool in LSM in the study area. The results of proposed novel methodological framework can be used by decision-makers and forest engineers for forest management spatially forest roads conservation that have key importance in sustainable development in local and regional scales.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/139940