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Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran

This study presents the application of an artificial neural network (ANN) and geographic information system (GIS) for estimating the social vulnerability to earthquakes in the Tabriz city, Iran. Thereby, seven indicators were identified and used for earthquake vulnerability mapping, including popula...

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Main Authors: Alizadeh, Mohsen, Alizadeh, Esmaeil, Kotenaee, Sara Asadollahpour, Shahabi, Himan, Pour, Amin Beiranvand, Panahi, Mahdi, Ahmad, Baharin, Saro, Lee
Format: Article
Language:English
Published: MDPI AG 2018
Subjects:
HT101-395 Sociology, Urban
Online Access:http://eprints.utm.my/id/eprint/86196/1/MohsenAlizadeh2018_SocialVulnerabilityAssessmentUsingArtificialNeural.pdf
http://eprints.utm.my/id/eprint/86196/
http://dx.doi.org/10.3390/su10103376
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spelling my.utm.861962020-08-30T09:05:14Z http://eprints.utm.my/id/eprint/86196/ Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran Alizadeh, Mohsen Alizadeh, Esmaeil Kotenaee, Sara Asadollahpour Shahabi, Himan Pour, Amin Beiranvand Panahi, Mahdi Ahmad, Baharin Saro, Lee HT101-395 Sociology, Urban This study presents the application of an artificial neural network (ANN) and geographic information system (GIS) for estimating the social vulnerability to earthquakes in the Tabriz city, Iran. Thereby, seven indicators were identified and used for earthquake vulnerability mapping, including population density, household density, employed density, unemployed density, and literate people. To obtain more accuracy in our analysis, all of the indicators were entered into a geographic information system (GIS). After the standardization of the data, an artificial neural network (ANN) model was applied for deriving a social vulnerability map (SVM) of different hazard classes for Tabriz city. The results showed that 0.77% of the total area was found to be very highly vulnerable. Very low vulnerability was recorded for 76.31% of the total study area. The comparison of data provided by (SVM) and the residential building vulnerability (RBV) of Tabriz city indicated the validity of the results obtained by ANN processes. Scatter plots are used to plot the data. These scatter plots indicate the existence of a strong positive relationship between the most vulnerable zones (1, 4, and 5) and the least (3, 7, and 9) of the SVM and RBV. The results highlight the importance of using social vulnerability study for defining seismic-risk mitigation policies, emergency management, and territorial planning in order to reduce the impacts of disasters. MDPI AG 2018-09-21 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/86196/1/MohsenAlizadeh2018_SocialVulnerabilityAssessmentUsingArtificialNeural.pdf Alizadeh, Mohsen and Alizadeh, Esmaeil and Kotenaee, Sara Asadollahpour and Shahabi, Himan and Pour, Amin Beiranvand and Panahi, Mahdi and Ahmad, Baharin and Saro, Lee (2018) Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran. Sustainability (Switzerland), 10 (10). ISSN 2071-1050 http://dx.doi.org/10.3390/su10103376 DOI:10.3390/su10103376
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic HT101-395 Sociology, Urban
spellingShingle HT101-395 Sociology, Urban
Alizadeh, Mohsen
Alizadeh, Esmaeil
Kotenaee, Sara Asadollahpour
Shahabi, Himan
Pour, Amin Beiranvand
Panahi, Mahdi
Ahmad, Baharin
Saro, Lee
Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran
description This study presents the application of an artificial neural network (ANN) and geographic information system (GIS) for estimating the social vulnerability to earthquakes in the Tabriz city, Iran. Thereby, seven indicators were identified and used for earthquake vulnerability mapping, including population density, household density, employed density, unemployed density, and literate people. To obtain more accuracy in our analysis, all of the indicators were entered into a geographic information system (GIS). After the standardization of the data, an artificial neural network (ANN) model was applied for deriving a social vulnerability map (SVM) of different hazard classes for Tabriz city. The results showed that 0.77% of the total area was found to be very highly vulnerable. Very low vulnerability was recorded for 76.31% of the total study area. The comparison of data provided by (SVM) and the residential building vulnerability (RBV) of Tabriz city indicated the validity of the results obtained by ANN processes. Scatter plots are used to plot the data. These scatter plots indicate the existence of a strong positive relationship between the most vulnerable zones (1, 4, and 5) and the least (3, 7, and 9) of the SVM and RBV. The results highlight the importance of using social vulnerability study for defining seismic-risk mitigation policies, emergency management, and territorial planning in order to reduce the impacts of disasters.
format Article
author Alizadeh, Mohsen
Alizadeh, Esmaeil
Kotenaee, Sara Asadollahpour
Shahabi, Himan
Pour, Amin Beiranvand
Panahi, Mahdi
Ahmad, Baharin
Saro, Lee
author_facet Alizadeh, Mohsen
Alizadeh, Esmaeil
Kotenaee, Sara Asadollahpour
Shahabi, Himan
Pour, Amin Beiranvand
Panahi, Mahdi
Ahmad, Baharin
Saro, Lee
author_sort Alizadeh, Mohsen
title Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran
title_short Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran
title_full Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran
title_fullStr Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran
title_full_unstemmed Social vulnerability assessment using artificial neural network (ANN) model for earthquake hazard in Tabriz city, Iran
title_sort social vulnerability assessment using artificial neural network (ann) model for earthquake hazard in tabriz city, iran
publisher MDPI AG
publishDate 2018
url http://eprints.utm.my/id/eprint/86196/1/MohsenAlizadeh2018_SocialVulnerabilityAssessmentUsingArtificialNeural.pdf
http://eprints.utm.my/id/eprint/86196/
http://dx.doi.org/10.3390/su10103376
_version_ 1677781144355995648
score 13.211869

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