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Earthquake Damage Assessment Based on User Generated Data in Social Networks

Author

Listed:
  • Sajjad Ahadzadeh

    (GIS Department, Faculty of Geodesy & Geomatics Engineering, K.N.Toosi University of Technology, Tehran 19967-15433, Iran)

  • Mohammad Reza Malek

    (GIS Department, Faculty of Geodesy & Geomatics Engineering, K.N.Toosi University of Technology, Tehran 19967-15433, Iran)

Abstract

Natural disasters have always been one of the threats to human societies. As a result of such crises, many people will be affected, injured, and many financial losses will incur. Large earthquakes often occur suddenly; consequently, crisis management is difficult. Quick identification of affected areas after critical events can help relief workers to provide emergency services more quickly. This paper uses social media text messages to create a damage map. A support vector machine (SVM) machine-learning method was used to identify mentions of damage among social media text messages. The damage map was created based on damage-related tweets. The results showed the SVM classifier accurately identified damage-related messages where the F-score attained 58%, precision attained 56.8%, recall attained 59.25%, and accuracy attained 71.03%. In addition, the temporal pattern of damage and non-damage tweets was investigated on each day and per hour. The results of the temporal analysis showed that most damage-related messages were sent on the day of the earthquake. The results of our research were evaluated by comparing the created damage map with official intensity maps. The findings showed that the damage of the earthquake can be estimated efficiently by our strategy at multispatial units with an overall accuracy of 69.89 at spatial grid unit and Spearman’s rho and Pearson correlation of 0.429 and 0.503, respectively, at the spatial county unit. We used two spatial units in this research to examine the impact of the spatial unit on the accuracy of damage assessment. The damage map created in this research can determine the priority of the relief workers.

Suggested Citation

  • Sajjad Ahadzadeh & Mohammad Reza Malek, 2021. "Earthquake Damage Assessment Based on User Generated Data in Social Networks," Sustainability, MDPI, vol. 13(9), pages 1-19, April.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4814-:d:543160
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    References listed on IDEAS

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    1. Lei Zou & Nina S. N. Lam & Heng Cai & Yi Qiang, 2018. "Mining Twitter Data for Improved Understanding of Disaster Resilience," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 108(5), pages 1422-1441, September.
    2. Yu Zhang & Wenzhou Wu & Qi Wang & Fenzhen Su, 2017. "A Geo-Event-Based Geospatial Information Service: A Case Study of Typhoon Hazard," Sustainability, MDPI, vol. 9(4), pages 1-18, March.
    3. Marco Avvenuti & Stefano Cresci & Fabio Del Vigna & Tiziano Fagni & Maurizio Tesconi, 2018. "CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing," Information Systems Frontiers, Springer, vol. 20(5), pages 993-1011, October.
    4. Zihao Wen & Hui Zhang & Ronghui Zhang, 2021. "Safety-Critical Event Identification on Mountain Roads for Traffic Safety and Environmental Protection Using Support Vector Machine with Information Entropy," Sustainability, MDPI, vol. 13(8), pages 1-15, April.
    5. Jose Luis Fernandez-Marquez & Chiara Francalanci & Sharada Mohanty & Rosy Mondardini & Barbara Pernici & Gabriele Scalia, 2019. "E2mC: Improving Rapid Mapping with Social Network Information," Lecture Notes in Information Systems and Organization, in: Federico Cabitza & Carlo Batini & Massimo Magni (ed.), Organizing for the Digital World, pages 63-74, Springer.
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    Cited by:

    1. Hamid Bahadori & Hamed Vahdat-Nejad & Hossein Moradi, 2022. "CrowdBIG: crowd-based system for information gathering from the earthquake environment," 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. 114(3), pages 3719-3741, December.

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