Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran

Mohsen Alizadeh, Esmaeil Alizadeh, Sara Asadollahpour Kotenaee, Himan Shahabi, Amin Beiranvand Pour, Mahdi Panahi, Baharin Bin Ahmad and Lee Saro
Additional contact information
Mohsen Alizadeh: Department of Urban Regional Planning, Faculty of Built Environment, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia
Esmaeil Alizadeh: Faculty of Business and Economic, Technische Universitat Bergakademie Freiberg, 09599 Freiber, Germany
Sara Asadollahpour Kotenaee: Department of Urban Planning, Faculty of Architecture, Civil, Art, Islamic Azad University of Science and Research Branch, Tehran 14778-93855, Iran
Himan Shahabi: Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
Amin Beiranvand Pour: Korea Polar Research Institute (KOPRI), Songdomirae-ro, Yeonsu-gu, Incheon 21990, Korea
Mahdi Panahi: Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran P.O. Box 19585/466, Iran
Baharin Bin Ahmad: Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia
Lee Saro: Geological Research Division, Korea Institute of Geoscience & Mineral Resources (KIGAM), Daejeon 34132, Korea

Sustainability, 2018, vol. 10, issue 10, 1-23

Abstract: 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.

Keywords: earthquake hazard; social vulnerability map (SVM); artificial neural network (ANN); Tabriz (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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