A New Approach To Anticipating Flood Damage

A recent research paper presents a ‘proof of concept’ for predicting flood damage probabilities - ‘ using only open-source data and machine learning methods - ‘ that the authors say can ‘fill in gaps of unreported or unaccounted flood damage, identify unexpected damage, and rapidly update estimates [of flood damage probabilities] as new information becomes available.’

‘This is the first spatially complete map of flood damage probability for the United States’ said Ross Meentemeyer, professor of geospatial analytics at North Carolina State University Center and an author of the paper, adding that the information it provides can be used to learn more about flood risk in vulnerable, underrepresented communities.

Spearheaded by Elyssa Collins, a doctoral candidate in the North Carolina State University Center for Geospatial Analytics, the research used machine learning - ‘ a type of artificial intelligence that employs algorithms to automatically update data, in effect training the program.

Inputs included flood severity, climate, and socio-economic exposure, among others.

About 90 percent of all U.S. natural disasters involve flooding. Improved decision-making around mitigation and resilience requires improved data.