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

Remote Sensing

Milad Mohammad Nataj¹  Philip K Langat²  Iman Khosravi³  Asad Asadzadeh⁴  Hasan Zabihi⁵  Mohsen Alizadeh⁵  Fatemeh Rezaie⁶  Amin Beiranvand Pour⁷  Isabelle D. Wolf⁸  Biswajeet Pradhan⁹ 

[1] Department of Civil Engineering, Sharif University of Technology, Azadi Ave, Tehran 11365-11155, Iran;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;Department of Urban Planning and Land Management, Institute of Geodesy and Geoinformation (IGG), University of Bonn, Nußallee 1, 53115 Bonn, Germany;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;Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Terengganu, Malaysia;School of Geography and Sustainable Communities, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia;The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia;

关键词: earthquake;    vulnerability assessment;    urban areas;    ANN;    SWOT;    QSPM;   

DOI  :  10.3390/rs13224519

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown