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Enhancing Machine Learning Explainability of Disaster Preparedness Models from the FEMA National Household Survey to
Taryn Amberson1, Wenhui Zhang2, Samuel E Sondheim3,4
1Department of Health Systems and Population Health, University of Washington, Seattle, Washington, USA.
Population Health Management
|April 7, 2025
Summary
Detailed evacuation and shelter plans, flood insurance, and education significantly boost household disaster preparedness. Targeted TV information aids older Black adults. This validates ML models for public health equity.
Area of Science:
- Public Health
- Disaster Management
- Health Equity
Background:
- Disasters cause significant mortality, morbidity, and economic losses in the U.S.
- Household disaster preparedness is crucial for mitigating these impacts.
- Existing machine learning (ML) models can inform preparedness strategies.
Purpose of the Study:
- To validate a preexisting machine learning (ML) model for household disaster preparedness using updated data.
- To identify key features influencing disaster preparedness.
- To explore tailored strategies for vulnerable subpopulations.
Main Methods:
- Harmonized Federal Emergency Management Agency (FEMA) National Household Survey data (2021-2023).
- Transferred important features from a random forest ML model to multiple linear and logistic regression models.
- Analyzed associations between preparedness factors and overall disaster preparedness, including stratified analysis for older Black adults.
Main Results:
- Multiple regression models explained 42%-53% of the variance in household disaster preparedness.
- Key predictors included detailed evacuation plans (OR=3.5-5.5), shelter plans (OR=4.3-11.0), flood insurance (OR=1.5-2.0), and higher education (OR=1.1).
- Lack of a specified disaster information source reduced preparedness (OR=0.11-0.53). Television viewing increased preparedness for older Black adults (OR=2.2).
Conclusions:
- Validates the importance of detailed planning and flood insurance for disaster preparedness.
- Highlights the need for tailored education for older adults with lower educational attainment.
- Recommends targeted media strategies for specific subpopulations, enhancing population health equity.
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