New tool helps responders identify highest-risk areas for post-hurricane rescue efforts

Researchers have created a mathematical model to predict which neighborhoods should be prioritized for search and rescue operations immediately after a hurricane. This tool aims to speed up recovery efforts by Coast Guard or other responders during the critical first 48–72 hours following a major disaster like a hurricane. The study, published in the International Journal of Disaster Risk Reduction, is led by researchers from North Carolina State University.

Co-author Brandon McConnell explains that in the immediate aftermath of a hurricane, emergency responders often arrive from across the country but may not have comprehensive information to guide their initial efforts. The team developed a predictive modeling framework focused on identifying census tracts where residents are most likely to need rescue.

This model incorporates U.S. Census data to pinpoint populations that are more vulnerable due to factors like physical disabilities or financial constraints, as well as National Flood Insurance Program data to highlight areas with higher flood risks.

The researchers validated their framework using Hurricane Harvey, a Category 4 storm that devastated the Houston area in 2017. By feeding regional Census data and National Flood Insurance Program data into their model, they were able to pinpoint census tracts most likely to contain residents trapped by floodwaters. When compared against actual rescue locations in the wake of Hurricane Harvey, the model's predictions were largely accurate, showing its potential utility in real-world rescue planning.

While the initial version of the tool is not perfect and could benefit from additional refinement—especially with access to more detailed data—it already demonstrates significant promise. According to lead author Patrick Leavitt, the model can be run in real-time as responders deploy, providing them with immediate insights to prioritize their efforts.

This dual utility—being beneficial during both the response and planning phases of disaster management—highlights the tool's potential to be a valuable asset for emergency managers and disaster response teams.