Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran

Sangchini, EK; Emami, SN; Tahmasebipour, N; Pourghasemi, HR; Naghibi, SA; Arami, SA; Pradhan, B

Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran

Sangchini, EK Emami, SN Tahmasebipour, N Pourghasemi, HR Naghibi, SA Arami, SA Pradhan, B

Permalink

Publisher:: Springer Berlin Heidelberg
Publication Type:: Journal Article
Citation:: Arabian Journal of Geosciences, 2016, 9, (3), pp. 201
Issue Date:: 2016

Closed Access

	Filename	Description	Size
	Sangchini2016_Article_AssessmentAndComparisonOfCombi.pdf	Published version	3.03 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	Sangchini, EK
dc.contributor.author	Emami, SN
dc.contributor.author	Tahmasebipour, N
dc.contributor.author	Pourghasemi, HR
dc.contributor.author	Naghibi, SA
dc.contributor.author	Arami, SA
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2022-07-14T22:50:07Z
dc.date.available	2022-07-14T22:50:07Z
dc.date.issued	2016
dc.identifier.citation	Arabian Journal of Geosciences, 2016, 9, (3), pp. 201
dc.identifier.issn	1866-7511
dc.identifier.issn	1866-7538
dc.identifier.uri	http://hdl.handle.net/10453/158927
dc.description.abstract	Landslide is one of the most important natural hazards that make numerous financial damages and life losses each year in the worldwide. Identifying the susceptible areas and prioritizing them in order to provide an efficient susceptibility management is very vital. In current study, a comparative analysis was made between combined bivariate and AHP models (bivariate-AHP) with a logistic regression. At first, landslide inventory map of the study area was prepared using extensive field surveys and aerial photographs interpretation. In the next step, nine landslide causative factors were selected including altitude, slope percentage, slope aspect, lithology, distance from faults, streams and roads, land use, and precipitation which affect occurrence of the landslides in the study area. Subsequently, landslide susceptibility maps were produced using weighted (AHP) bivariate and logistic regression models. Finally, receiver operating characteristics (ROC) curve was used in order to evaluate the prediction capability of the mentioned models for landslide susceptibility mapping. According to the results, the combined bivariate and AHP models provided slightly higher prediction accuracy than logistic regression model. The combined bivariate and AHP, and logistic regression models had the area under the curve (AUC-ROC) values of 0.914, and 0.865, respectively. The resultant landslide susceptibility maps can be useful in appropriate watershed management practices and for sustainable development in the regions with similar conditions.
dc.language	English
dc.publisher	Springer Berlin Heidelberg
dc.relation.ispartof	Arabian Journal of Geosciences
dc.relation.isbasedon	10.1007/s12517-015-2258-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0403 Geology
dc.title	Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0403 Geology
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 Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-14T22:50:05Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	3

Abstract:

Landslide is one of the most important natural hazards that make numerous financial damages and life losses each year in the worldwide. Identifying the susceptible areas and prioritizing them in order to provide an efficient susceptibility management is very vital. In current study, a comparative analysis was made between combined bivariate and AHP models (bivariate-AHP) with a logistic regression. At first, landslide inventory map of the study area was prepared using extensive field surveys and aerial photographs interpretation. In the next step, nine landslide causative factors were selected including altitude, slope percentage, slope aspect, lithology, distance from faults, streams and roads, land use, and precipitation which affect occurrence of the landslides in the study area. Subsequently, landslide susceptibility maps were produced using weighted (AHP) bivariate and logistic regression models. Finally, receiver operating characteristics (ROC) curve was used in order to evaluate the prediction capability of the mentioned models for landslide susceptibility mapping. According to the results, the combined bivariate and AHP models provided slightly higher prediction accuracy than logistic regression model. The combined bivariate and AHP, and logistic regression models had the area under the curve (AUC-ROC) values of 0.914, and 0.865, respectively. The resultant landslide susceptibility maps can be useful in appropriate watershed management practices and for sustainable development in the regions with similar conditions.

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

http://hdl.handle.net/10453/158927