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Leveraging Machine Learning and Simulation to Advance Disaster Preparedness Assessments through FEMA National Household Survey Data

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Listed:
  • Zhenlong Jiang

    (Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA)

  • Yudi Chen

    (Department of Management Science and Engineering, College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Ting-Yeh Yang

    (College of Engineering and Computing, George Mason University, Fairfax, VA 22030, USA)

  • Wenying Ji

    (Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA)

  • Zhijie (Sasha) Dong

    (Department of Construction Management, University of Houston, Houston, TX 77204, USA)

  • Ran Ji

    (Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA)

Abstract

Effective household and individual disaster preparedness can minimize physical harm and property damage during catastrophic events. To assess the risk and vulnerability of affected areas, it is crucial for relief agencies to understand the level of public preparedness. Traditionally, government agencies have employed nationwide random telephone surveys to gauge the public’s attitudes and actions towards disaster preparedness. However, these surveys may lack generalizability in certain affected locations due to low response rates or areas not covered by the survey. To address this issue and enhance the comprehensiveness of disaster preparedness assessments, we develop a framework that seamlessly integrates machine learning and simulation. Our approach leverages machine learning algorithms to establish relationships between public attitudes towards disaster preparedness and demographic characteristics. Using Monte Carlo simulation, we generate datasets that incorporate demographic information of the affected location based on government-provided demographic distribution data. The generated dataset is then input into the machine learning model to predict the disaster preparedness attitudes of the affected population. We demonstrate the effectiveness of our framework by applying it to Miami-Dade County, where it accurately predicts the level of disaster preparedness. With this innovative approach, relief agencies can have a clearer and more comprehensive understanding of public disaster preparedness.

Suggested Citation

  • Zhenlong Jiang & Yudi Chen & Ting-Yeh Yang & Wenying Ji & Zhijie (Sasha) Dong & Ran Ji, 2023. "Leveraging Machine Learning and Simulation to Advance Disaster Preparedness Assessments through FEMA National Household Survey Data," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8035-:d:1147372
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    References listed on IDEAS

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    1. Hoffmann, Roman & Muttarak, Raya, 2017. "Learn from the Past, Prepare for the Future: Impacts of Education and Experience on Disaster Preparedness in the Philippines and Thailand," World Development, Elsevier, vol. 96(C), pages 32-51.
    2. Vladimir M. Cvetković & Giulia Roder & Adem Öcal & Paolo Tarolli & Slavoljub Dragićević, 2018. "The Role of Gender in Preparedness and Response Behaviors towards Flood Risk in Serbia," IJERPH, MDPI, vol. 15(12), pages 1-21, December.
    3. Rawls, Carmen G. & Turnquist, Mark A., 2010. "Pre-positioning of emergency supplies for disaster response," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 521-534, May.
    4. Sıdıka Tekeli‐Yeşil & Necati Dedeoğlu & Charlotte Braun‐Fahrlaender & Marcel Tanner, 2010. "Factors Motivating Individuals to Take Precautionary Action for an Expected Earthquake in Istanbul," Risk Analysis, John Wiley & Sons, vol. 30(8), pages 1181-1195, August.
    5. Elçi, Özgün & Noyan, Nilay, 2018. "A chance-constrained two-stage stochastic programming model for humanitarian relief network design," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 55-83.
    6. Michal Titko & Jozef Ristvej, 2020. "Assessing Importance of Disaster Preparedness Factors for Sustainable Disaster Risk Management: The Case of the Slovak Republic," Sustainability, MDPI, vol. 12(21), pages 1-20, November.
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