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

Author

Listed:
  • 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
    Department of Geophysical Exploration, Korea University of Science and Technology, Daejeon 34113, Korea)

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.

Suggested Citation

  • Mohsen Alizadeh & Esmaeil Alizadeh & Sara Asadollahpour Kotenaee & Himan Shahabi & Amin Beiranvand Pour & Mahdi Panahi & Baharin Bin Ahmad & Lee Saro, 2018. "Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran," Sustainability, MDPI, vol. 10(10), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3376-:d:171231
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    References listed on IDEAS

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    2. Yuxin Gao & Xianrui Yu & Menghao Xi & Qiuhong Zhao, 2023. "Assessment of Vulnerability Caused by Earthquake Disasters Based on DEA: A Case Study of County-Level Units in Chinese Mainland," Sustainability, MDPI, vol. 15(9), pages 1-15, May.
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    4. Hyung-Sup Jung & Saro Lee & Biswajeet Pradhan, 2020. "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations," Sustainability, MDPI, vol. 12(6), pages 1-6, March.
    5. Jihye Han & Soyoung Park & Seongheon Kim & Sanghun Son & Seonghyeok Lee & Jinsoo Kim, 2019. "Performance of Logistic Regression and Support Vector Machines for Seismic Vulnerability Assessment and Mapping: A Case Study of the 12 September 2016 ML5.8 Gyeongju Earthquake, South Korea," Sustainability, MDPI, vol. 11(24), pages 1-19, December.
    6. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    7. Navdeep Agrawal & Laxmi Gupta & Jagabandhu Dixit, 2021. "Assessment of the Socioeconomic Vulnerability to Seismic Hazards in the National Capital Region of India Using Factor Analysis," Sustainability, MDPI, vol. 13(17), pages 1-19, August.
    8. Gordana Pavić & Marijana Hadzima-Nyarko & Borko Bulajić, 2020. "A Contribution to a UHS-Based Seismic Risk Assessment in Croatia—A Case Study for the City of Osijek," Sustainability, MDPI, vol. 12(5), pages 1-24, February.
    9. Md. Abul Kalam Azad & Abu Reza Md. Towfiqul Islam & Md. Siddiqur Rahman & Kurratul Ayen, 2021. "Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 1109-1135, August.
    10. Mohammad Ilbeigi & Sarath Chandra K. Jagupilla, 2020. "An Empirical Analysis of Association between Socioeconomic Factors and Communities’ Exposure to Natural Hazards," Sustainability, MDPI, vol. 12(16), pages 1-13, August.
    11. Jihye Han & Jinsoo Kim & Soyoung Park & Sanghun Son & Minji Ryu, 2020. "Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    12. Xin Wei & Niaz Muhammad Shahani & Xigui Zheng, 2023. "Predictive Modeling of the Uniaxial Compressive Strength of Rocks Using an Artificial Neural Network Approach," Mathematics, MDPI, vol. 11(7), pages 1-17, March.

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