Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping

Paryani, S; Neshat, A; Javadi, S; Pradhan, B

Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping

Paryani, S Neshat, A Javadi, S Pradhan, B

Permalink

Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Natural Hazards, 2020, 103, (2), pp. 1961-1988
Issue Date:: 2020-09-01

Closed Access

	Filename	Description	Size
	Paryani2020_Article_ComparativePerformanceOfNewHyb.pdf	Published version	3.07 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	Paryani, S
dc.contributor.author	Neshat, A
dc.contributor.author	Javadi, S
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2020-11-22T19:54:49Z
dc.date.available	2020-11-22T19:54:49Z
dc.date.issued	2020-09-01
dc.identifier.citation	Natural Hazards, 2020, 103, (2), pp. 1961-1988
dc.identifier.issn	0921-030X
dc.identifier.issn	1573-0840
dc.identifier.uri	http://hdl.handle.net/10453/144223
dc.description.abstract	© 2020, Springer Nature B.V. Abstract: Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the high potential of landslides in the region. The aim of the study was to combine adaptive neuro-fuzzy inference system (ANFIS) with grey wolf optimizer (GWO) and particle swarm optimizer (PSO) algorithms using the outputs of qualitative stepwise weight assessment ratio analysis (SWARA) and quantitative certainty factor (CF) models. To this end, 264 landslide positions and twelve conditioning factors including slope, aspect, altitude, distance to faults, distance to rivers, distance to roads, land use, lithology, rainfall, plan and profile curvature and TWI were then extracted considering regional characteristics, literature review and available data. In the next step, the multi-criteria SWARA decision-making model and CF probability model were used to evaluate a correlation between landslide distribution and conditioning factors. Ultimately, landslide susceptibility maps were generated by ANFIS-GWO and ANFIS-PSO hybrid models and the accuracy of models was assessed by ROC curve. According to the results, the area under the curve (AUC) for the hybrid models ANFIS - GWO SWARA, ANFIS - PSO SWARA, ANFIS - GWO CF and ANFIS - PSO CF was 0.789, 0.838, 0.850 and 0.879, respectively. The hybrid models ANFIS - PSO CF and ANFIS - GWO SWARA showed the highest and lowest prediction rate, respectively. Moreover, CF outperformed the SWARA method in terms of evaluating correlation between conditioning factors and landslides. The map produced in this study can be used by regional authorities to manage landslide risk. Graphic abstract: [Figure not available: see fulltext.].
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Natural Hazards
dc.relation.isbasedon	10.1007/s11069-020-04067-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0401 Atmospheric Sciences, 0406 Physical Geography and Environmental Geoscience, 1701 Psychology
dc.subject.classification	Strategic, Defence & Security Studies
dc.title	Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping
dc.type	Journal Article
utslib.citation.volume	103
utslib.for	0401 Atmospheric Sciences
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	1701 Psychology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
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
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-22T19:54:44Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	103
utslib.citation.issue	2

Abstract:

© 2020, Springer Nature B.V. Abstract: Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the high potential of landslides in the region. The aim of the study was to combine adaptive neuro-fuzzy inference system (ANFIS) with grey wolf optimizer (GWO) and particle swarm optimizer (PSO) algorithms using the outputs of qualitative stepwise weight assessment ratio analysis (SWARA) and quantitative certainty factor (CF) models. To this end, 264 landslide positions and twelve conditioning factors including slope, aspect, altitude, distance to faults, distance to rivers, distance to roads, land use, lithology, rainfall, plan and profile curvature and TWI were then extracted considering regional characteristics, literature review and available data. In the next step, the multi-criteria SWARA decision-making model and CF probability model were used to evaluate a correlation between landslide distribution and conditioning factors. Ultimately, landslide susceptibility maps were generated by ANFIS-GWO and ANFIS-PSO hybrid models and the accuracy of models was assessed by ROC curve. According to the results, the area under the curve (AUC) for the hybrid models ANFIS - GWO SWARA, ANFIS - PSO SWARA, ANFIS - GWO CF and ANFIS - PSO CF was 0.789, 0.838, 0.850 and 0.879, respectively. The hybrid models ANFIS - PSO CF and ANFIS - GWO SWARA showed the highest and lowest prediction rate, respectively. Moreover, CF outperformed the SWARA method in terms of evaluating correlation between conditioning factors and landslides. The map produced in this study can be used by regional authorities to manage landslide risk. Graphic abstract: [Figure not available: see fulltext.].

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

http://hdl.handle.net/10453/144223