The ability to forecast a major flooding event like Hurricane Florence has improved significantly. But understanding how such a storm will interact with the built environment and affect people living in a specific area is still quite limited.
The factors involved in predicting flood scenarios are changing faster than tools that help people prepare and adapt. For example, we know baseline sea levels made higher by climate change will mean bigger storm surges from hurricanes.
But human activity has intensified disasters in other ways, too. Population has exploded along U.S. coastlines, exposing far more people and infrastructure to related threats. Stephen Strader, an assistant professor of geography and the environment at Villanova University, calls this the “expanding bull’s-eye effect.”
“Societal growth is the biggest influence on disasters because it increases the potential for loss,” Strader says. It also means more of the environment has been paved over by human habitation, changing the hydrology of major floods in ways that are not well measured or communicated.
Craig Fugate thinks a lot about the interplay of these issues. He led the Federal Emergency Management Agency (FEMA) from 2009 to 2017. And as a Floridian he sees the lingering effects of previous hurricanes, as well as how rising seas are making flooding more routine—and displacing certain populations in the process.
In his post-FEMA career, Fugate is now Chief Emergency Manager of a start-up called One Concern, which he says is using big data and machine learning to help communities and businesses better prepare for threats such as flooding—and not just single, extreme events like Florence, but for the more routine problems climate change will bring.
One Concern says its flooding platform, which the company plans to release by the end of this year, will help predict inundation levels up to five days in advance of a storm—at a block-by-block level. This kind of resolution could make it easier to prepare for storms and adapt to future threats with much greater specificity. Fugate told Scientific American about the project this summer.
[An edited transcript of the interview follows.]
After your time at FEMA and seeing the 30,000-foot view of disaster response in America, what drew you to an AI start-up?
I want to change outcomes. How do we prepare for future risk when all of our data for FEMA maps is looking backwards? What I was fascinated by with the [One Concern] flood model was that it’s being designed to be a response tool. It takes the rainfall estimates and instead of just saying, “You’re going to have flash flooding,” it predicts that this neighborhood is going to see three to five feet of flooding and that neighborhood might see up to 20 feet. We can also know which areas have had heavy impacts right away—decision makers don’t have to wait for information to come in before activating response.
What really sold it to me is how much data is involved, and how we can see things at high-resolution, and quickly. We can run various scenarios in the days before a storm arrives and understand when and how systems would fail. Using AI lowers the threshold to do the “what-ifs.”
What also got me interested is we did a lot of, quote–unquote, “mitigation projects” in the New Jersey area after [Superstorm] Sandy. And a lot of those projects [such as elevating structures] were based upon looking at a 100-year cost-benefit analysis; our assumption is that they will perform better in the next storm. But we could never really ask the question, “How much better will it do?”
The term “resilience” gets tossed around a lot when we talk about preparing our coastal communities for sea level rise—in some cases by moving people permanently out of harm’s way with buyouts. How can these models make resilience less abstract?
If we’re always waiting until after disasters to move people, then that’s going to be the most painful and disruptive way to do it.
