Spatial earthquake vulnerability assessment by using multi-criteria decision making and probabilistic neural network techniques in Odisha, India

Jena, R; Pradhan, B; Beydoun, G; Alamri, A; Shanableh, A

Spatial earthquake vulnerability assessment by using multi-criteria decision making and probabilistic neural network techniques in Odisha, India

Jena, R Pradhan, B

Beydoun, G

Alamri, A Shanableh, A

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Publisher:: TAYLOR & FRANCIS LTD
Publication Type:: Journal Article
Citation:: Geocarto International, 2022
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Jena, R
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Beydoun, G https://orcid.org/0000-0001-8087-5445
dc.contributor.author	Alamri, A
dc.contributor.author	Shanableh, A
dc.date.accessioned	2022-03-16T21:38:52Z
dc.date.available	2022-03-16T21:38:52Z
dc.date.issued	2022-01-01
dc.identifier.citation	Geocarto International, 2022
dc.identifier.issn	1010-6049
dc.identifier.issn	1752-0762
dc.identifier.uri	http://hdl.handle.net/10453/155271
dc.description.abstract	In this study, the multi-criteria decision-making method was used to estimate the weights of several input factors such as slope, curvature, elevation, proximity to road, road density, proximity to land use, land use density, proximity to water bodies, river density, rail density, distance from rail, groundwater variation, lithology with amplification factors, peak ground acceleration (PGA) variation, and population density. An integrated analytic hierarchy process (AHP) and a probabilistic neural network (PNN) were applied for the Earthquake vulnerability assessment (EVA). The PNN model successfully explored the relationship between variables and weights obtained from the AHP approach. Validation results indicate that 92.5% accuracy was attained by the PNN model. According to the results, 24.26%, 15.26%, and 20.58% of the area fall under very-high, high, and moderate vulnerability category, respectively. The EVA map illustrates that high to very-high impact could be observed in coastal Odisha and few districts in the Mahanadi Graven.
dc.language	English
dc.publisher	TAYLOR & FRANCIS LTD
dc.relation.ispartof	Geocarto International
dc.relation.isbasedon	10.1080/10106049.2021.1992023
dc.rights	“This is an Accepted Manuscript of an article published by Taylor & Francis in [Geocarto International, 2022], available online: https://www.tandfonline.com/doi/full/10.1080/10106049.2021.1992023”
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Spatial earthquake vulnerability assessment by using multi-criteria decision making and probabilistic neural network techniques in Odisha, India
dc.type	Journal Article
utslib.for	0909 Geomatic Engineering
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
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2023-01-01T00:00:00+1000Z
dc.date.updated	2022-03-16T21:38:51Z
pubs.publication-status	Published

Abstract:

In this study, the multi-criteria decision-making method was used to estimate the weights of several input factors such as slope, curvature, elevation, proximity to road, road density, proximity to land use, land use density, proximity to water bodies, river density, rail density, distance from rail, groundwater variation, lithology with amplification factors, peak ground acceleration (PGA) variation, and population density. An integrated analytic hierarchy process (AHP) and a probabilistic neural network (PNN) were applied for the Earthquake vulnerability assessment (EVA). The PNN model successfully explored the relationship between variables and weights obtained from the AHP approach. Validation results indicate that 92.5% accuracy was attained by the PNN model. According to the results, 24.26%, 15.26%, and 20.58% of the area fall under very-high, high, and moderate vulnerability category, respectively. The EVA map illustrates that high to very-high impact could be observed in coastal Odisha and few districts in the Mahanadi Graven.

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http://hdl.handle.net/10453/155271