COLLEGE STATION, Texas — Texas A&M researchers are using a new AI program to improve the management of disasters like hurricanes before, during, and after the events.
- Flood Genome is a AI-generated program that analyzes 50 years of flood data from across the country to identify flood-prone areas.
- With the help of this program, disaster agencies can prepare and mobilize resources to the most affected neighborhoods before a flood-event even happens.
- It also monitors which evacuation routes and destinations are being used the most, providing accurate and timely information to improve public safety during weather emergency events.
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Texas is no stranger to hurricanes and heavy flooding. Just a few weeks ago thousands were affected by flooding from Hurricane Beryl.
“Flood Genome is a machine learning based model that determines the level of susceptibility of different neighborhoods to flooding.”
But a new AI-based model could help mitigate flooding and save lives.
“They can look into which areas are susceptible for these flash floods to have an impact based on Flood Genome outputs. And then before an event even starts, they can mobilize resources for rescues in those areas, for providing early evacuation orders to those areas.”
Dr. Ali Mostafavi with Texas A&M University helped design Flood Genome. With the help of AI, they analyzed flooding data from the last 50 years to determine which areas are more susceptible to flooding and how that will change with population growth.
“Cities like College Station, Bryan grow fast, there is extensive development, but we don't implement this analysis of how the flood susceptibility of areas change.”
Dr. Mostafavi’s research found that flood-prone areas depend on three characteristics: distance from streams and rivers, elevation, historical rainfall.
“These are not features that we didn't know that influence flood susceptibility, but we did not have before Flood Genome, we did not have data driven methods that help us specify the level of flood risk based on these characteristics in an interpretable way.”
They’ve recently finalized their project and are pitching it to city, state, and local disaster preparedness groups across the country.