Data-Driven Bayesian Network Models of Hurricane Evacuation Decision Making
This work addresses hurricane evacuation decision-making for individuals and emergency planners, but it is incremental as it applies existing Bayesian Network methods to new data in this domain.
The researchers tackled the problem of modeling hurricane evacuation decisions by proposing data-driven Bayesian Network models using questionnaire data from Hurricanes Harvey and Irma, revealing that social influence plays a significant role in evacuation decisions.
Hurricanes cause significant economic and human costs, requiring individuals to make critical evacuation decisions under uncertainty and stress. To enhance the understanding of this decision-making process, we propose using Bayesian Networks (BNs) to model evacuation decisions during hurricanes. We collected questionnaire data from two significant hurricane events: Hurricane Harvey and Hurricane Irma. We employed a data-driven approach by first conducting variable selection using mutual information, followed by BN structure learning with two constraint-based algorithms. The robustness of the learned structures was enhanced by model averaging based on bootstrap resampling. We examined and compared the learned structures of both hurricanes, revealing potential causal relationships among key predictors of evacuation, including risk perception, information received from media, suggestions from family and friends, and neighbors evacuating. Our findings highlight the significant role of social influence, providing valuable insights into the process of evacuation decision-making. Our results demonstrate the applicability and effectiveness of data-driven BN modeling in evacuation decision making.