FLOODSU.S. Flood Damage Risk Is Underestimated
Researchers used artificial intelligence to predict where flood damage is likely to happen in the continental United States, suggesting that recent flood maps from FEMA do not capture the full extent of flood risk.
In a new study, North Carolina State University researchers used artificial intelligence to predict where flood damage is likely to happen in the continental United States, suggesting that recent flood maps from the Federal Emergency Management Agency do not capture the full extent of flood risk.
In the study, published in Environmental Research Letters, researchers found a high probability of flood damage – including monetary damage, human injury and loss of life – for more than a million square miles of land across the United States across a 14-year period. That was more than 790,000 square miles greater than flood risk zones identified by FEMA’s maps.
“We’re seeing that there’s a lot of flood damage being reported outside of the 100-year floodplain,” said the study’s lead author Elyssa Collins, a doctoral candidate in the NC State Center for Geospatial Analytics. “There are a lot of places that are susceptible to flooding, and because they’re outside the floodplain, that means they do not have to abide by insurance, building code and land-use requirements that could help protect people and property.”
It can cost FEMA as much as $11.8 billion to create national Flood Insurance Rate Maps, which show whether an area has at least a 1% chance of flooding in a year, according to a 2020 report from the Association of State Floodplain Managers. Researchers say their method of using machine learning tools to estimate flood risk offers a way of rapidly updating flood maps as conditions change or more information becomes available.
“This is the first spatially complete map of flood damage probability for the United States; wall-to-wall information that can be used to learn more about flood risk in vulnerable, underrepresented communities,” said Ross Meentemeyer, Goodnight Distinguished Professor of Geospatial Analytics at NC State.
To create their computer models, researchers used reported data of flood damage for the United States, along with other information such as whether land is close to a river or stream, type of land cover, soil type and precipitation. The computer was able to “learn” from actual reports of damage to predict areas of high flood damage likelihood for each pixel of mapped land. They created separate models for each watershed in the United States.
