Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model

Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An...

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Autores principales:	Mohsen Alizadeh, Hasan Zabihi, Fatemeh Rezaie, Asad Asadzadeh, Isabelle D. Wolf, Philip K Langat, Iman Khosravi, Amin Beiranvand Pour, Milad Mohammad Nataj, Biswajeet Pradhan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	earthquake vulnerability assessment urban areas ANN SWOT QSPM Science Q
Acceso en línea:	https://doaj.org/article/bef10e4b1cb9455290c0fee240b97bd6
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id	oai:doaj.org-article:bef10e4b1cb9455290c0fee240b97bd6
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spelling	oai:doaj.org-article:bef10e4b1cb9455290c0fee240b97bd62021-11-25T18:53:52ZEarthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model10.3390/rs132245192072-4292https://doaj.org/article/bef10e4b1cb9455290c0fee240b97bd62021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4519https://doaj.org/toc/2072-4292Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies.Mohsen AlizadehHasan ZabihiFatemeh RezaieAsad AsadzadehIsabelle D. WolfPhilip K LangatIman KhosraviAmin Beiranvand PourMilad Mohammad NatajBiswajeet PradhanMDPI AGarticleearthquakevulnerability assessmenturban areasANNSWOTQSPMScienceQENRemote Sensing, Vol 13, Iss 4519, p 4519 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	earthquake vulnerability assessment urban areas ANN SWOT QSPM Science Q
spellingShingle	earthquake vulnerability assessment urban areas ANN SWOT QSPM Science Q Mohsen Alizadeh Hasan Zabihi Fatemeh Rezaie Asad Asadzadeh Isabelle D. Wolf Philip K Langat Iman Khosravi Amin Beiranvand Pour Milad Mohammad Nataj Biswajeet Pradhan Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model
description	Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies.
format	article
author	Mohsen Alizadeh Hasan Zabihi Fatemeh Rezaie Asad Asadzadeh Isabelle D. Wolf Philip K Langat Iman Khosravi Amin Beiranvand Pour Milad Mohammad Nataj Biswajeet Pradhan
author_facet	Mohsen Alizadeh Hasan Zabihi Fatemeh Rezaie Asad Asadzadeh Isabelle D. Wolf Philip K Langat Iman Khosravi Amin Beiranvand Pour Milad Mohammad Nataj Biswajeet Pradhan
author_sort	Mohsen Alizadeh
title	Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model
title_short	Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model
title_full	Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model
title_fullStr	Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model
title_full_unstemmed	Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model
title_sort	earthquake vulnerability assessment for urban areas using an ann and hybrid swot-qspm model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bef10e4b1cb9455290c0fee240b97bd6
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Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model

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