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

Zhenlong Jiang, Yudi Chen, Ting-Yeh Yang, Wenying Ji, Zhijie (Sasha) Dong and Ran Ji ()
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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

Sustainability, 2023, vol. 15, issue 10, 1-17

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.

Keywords: disaster management; household preparedness prediction; machine learning; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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