“In the first 48 hours of a major disaster like a hurricane, responders show up from all over the country to help and are often operating in an information vacuum,” says Brandon McConnell, co-author of the study and an associate research professor in NC State’s Edward P. Fitts Department of Industrial and Systems Engineering. The model was built specifically to address that vacuum, giving operational planners a data-driven starting point for deciding where to send boats, helicopters, and ground teams first, rather than relying on intuition or waiting for distress calls to accumulate.

The framework, described in a paper published open access in the International Journal of Disaster Risk Reduction under the title “Anticipating Household Rescue Demand in Hurricanes Using Socio-Demographic Data and Machine Learning,” rests on a straightforward but powerful premise: not all households face the same probability of needing rescue when a hurricane strikes. Decades of disaster research have identified factors that make people more vulnerable during such events, including physical disabilities that limit mobility, fewer financial resources that make evacuation difficult, lack of access to a vehicle, advanced age, and housing located in areas prone to flooding. The research team translated these established vulnerability factors into a predictive computational model.

Technically, the model draws on two complementary streams of publicly available federal data. U.S. Census data provide a fine-grained picture of the socio-demographic composition of each census tract, allowing the model to identify communities where a large share of residents are likely to have trouble evacuating in advance of a storm. National Flood Insurance Program data supply information on which areas face the greatest risk of flooding, indicating where those less-mobile populations are most likely to become trapped by rising water. By combining these inputs, the model produces a ranked map of expected rescue demand across an affected region before responders arrive on the scene.

A distinctive feature of the project is the perspective of its first author. Patrick Leavitt, who began the work while a graduate student at NC State, is an active-duty Coast Guard officer, and his first-hand experience with emergency response operations shaped how the team approached the problem. Rather than designing an abstract academic exercise, the researchers built the framework around the practical constraints responders face: the need for fast computation, the availability of data in real time, and the reality that every area will eventually be checked, but the order in which areas are searched can mean the difference between life and death for people trapped in attics or on rooftops.

“Specifically, we developed a predictive modeling framework to identify census tracts where residents are most likely to require rescue,” says Ben Rachunok, corresponding author of the paper and an assistant professor in NC State’s Fitts Department. “Responders will ultimately look in every area, but which areas are most likely to have people who require rescuing? If we can predict that, we can prioritize search efforts in those areas.” That prioritization logic is what distinguishes the tool from existing hazard maps, which typically show where flooding will occur but not where the intersection of flooding and human vulnerability will generate the greatest demand for rescue.

To test the framework, the researchers conducted a case study focused on Hurricane Harvey, the Category 4 storm that struck Texas in 2017 and caused catastrophic flooding across the Houston metropolitan area. Harvey is a particularly valuable test case because it produced one of the largest urban rescue operations in American history, with thousands of water rescues carried out by the Coast Guard, first responders, and volunteer rescuers. The team fed regional Census data and National Flood Insurance Program data into their model to generate predictions of which areas would be most likely to have residents trapped by floodwaters, and then compared those predicted high-priority zones against publicly available records of where rescues actually took place.

The comparison showed that the model performed well in identifying the areas where rescue demand concentrated. “Our framework did pretty well – it should be useful for responders in practice,” says Rachunok. “It’s not perfect, but even this version would be helpful – and we can put in the work to make it even better.” The researchers emphasize that the model is an initial version, and that its accuracy should improve with better data and continued refinement. Importantly, the model is computationally lightweight: it does not take long to run, which means it could be executed while responders are still deploying, handing them a prioritized list of search areas as soon as they arrive in the disaster zone.

“Being able to achieve these results with this initial version suggests we’re optimistic about its utility if we fine-tune the tool – particularly in instances where we have access to better data,” says McConnell. The team also sees applications beyond the immediate response phase. According to Leavitt, the research could support emergency managers during the planning phases of disaster preparedness, helping them develop response plans and design exercises that reflect realistic patterns of rescue demand. Because the model relies on data that are already collected and publicly available, jurisdictions could use it in advance of hurricane season to identify their most vulnerable communities and pre-position resources accordingly.

The researchers say they are open to collaborating with emergency management and disaster response leaders to improve the model itself and to make the tool more user-friendly for practical field use. As climate change increases the intensity of Atlantic hurricanes and coastal populations continue to grow, the demand for rapid, well-targeted search and rescue operations is only expected to rise. A predictive framework that turns census data and flood risk information into an actionable rescue priority map offers a glimpse of how data science can be woven into the earliest, most chaotic hours of disaster response – when every minute of saved search time can translate directly into lives saved.

Subject of Research: A predictive mathematical model that prioritizes census tracts for post-hurricane search and rescue operations using socio-demographic and flood risk data.

Article Title: New tool helps responders ID highest-risk areas for post-hurricane rescue efforts

Article References: New tool helps responders ID highest-risk areas for post-hurricane rescue efforts. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: hurricane, search and rescue, emergency response, flood risk, machine learning, census data, National Flood Insurance Program, Hurricane Harvey, disaster preparedness, Coast Guard, North Carolina State University, vulnerability mapping