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

Faculty of Built Environment & Surveying, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon 34132, Korea
Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
Department of Urban Planning and Land Management, Institute of Geodesy and Geoinformation (IGG), University of Bonn, Nußallee 1, 53115 Bonn, Germany
School of Geography and Sustainable Communities, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
Centre for Ecosystem Science, University of New South Wales, Sydney, NSW 2052, Australia
Department of Ecosystem Management, University of New England, Armidale, NSW 2351, Australia
Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan 81746-73441, Iran
Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Terengganu, Malaysia
Department of Civil Engineering, Sharif University of Technology, Azadi Ave, Tehran 11365-11155, Iran
The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
Center of Excellence for Climate Change Research, King Abdulaziz University, P.O. Box 80234, Jeddah 21589, Saudi Arabia
Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
Author to whom correspondence should be addressed.
Academic Editors: Bruno Adriano, Sadra Karimzadeh, Luis Moya, Bahareh Kalantar, Alok Bhardwaj and Yanbing Bai
Remote Sens. 2021, 13(22), 4519;
Received: 9 September 2021 / Revised: 4 November 2021 / Accepted: 5 November 2021 / Published: 10 November 2021
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. View Full-Text
Keywords: earthquake; vulnerability assessment; urban areas; ANN; SWOT; QSPM; Tabriz earthquake; vulnerability assessment; urban areas; ANN; SWOT; QSPM; Tabriz
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MDPI and ACS Style

Alizadeh, M.; Zabihi, H.; Rezaie, F.; Asadzadeh, A.; Wolf, I.D.; Langat, P.K.; Khosravi, I.; Beiranvand Pour, A.; Mohammad Nataj, M.; Pradhan, B. Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model. Remote Sens. 2021, 13, 4519.

AMA Style

Alizadeh M, Zabihi H, Rezaie F, Asadzadeh A, Wolf ID, Langat PK, Khosravi I, Beiranvand Pour A, Mohammad Nataj M, Pradhan B. Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model. Remote Sensing. 2021; 13(22):4519.

Chicago/Turabian Style

Alizadeh, Mohsen, Hasan Zabihi, Fatemeh Rezaie, Asad Asadzadeh, Isabelle D. Wolf, Philip K. Langat, Iman Khosravi, Amin Beiranvand Pour, Milad Mohammad Nataj, and Biswajeet Pradhan. 2021. "Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model" Remote Sensing 13, no. 22: 4519.

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