A Fresh Approach to Flood Prevention
COLLEGE STATION, Texas – At the forefront of technological advancements, artificial intelligence (AI) maintains its growth trajectory across diverse sectors, from healthcare to entertainment. Now, researchers at Texas A&M University are demonstrating how AI could become an essential tool in flood management, a concern faced by countless communities worldwide.
Professors at the university have created a pioneering AI program dedicated to improving disaster management strategies. The system targets flood scenarios both prior to and during an event, as well as addresses the aftermath.
Taking Control with Flood Genome
Dubbed “Flood Genome,” the AI program conducts an in-depth analysis of over 50 years of flood data collected nationwide. Its system identifies areas vulnerable to flooding, optimizing disaster relief agencies’ ability to prepare and mobilize resources even before a flooding event transpires.
The Flood Genome tracks and monitors evacuation routes most frequented by residents and provides vital, accurate, and timely information, thereby enhancing public safety during adverse weather events.
Leveraging AI for Public Safety
Flood events, such as the recent Hurricane Beryl, have impacted thousands of American citizens, emphasizing the urgency and importance of effective flood management. However, the deployment of the Flood Genome could significantly influence community resilience and response to such crises.
Using advanced machine learning, the Flood Genome delves into data archives to forecast potential neighborhood susceptibility to flash floods. In doing so, it creates an opportunity for necessary resources to be dispatched, and early evacuation orders to be issued in areas at imminent risk.
The Science Behind Flood Genome
In developing the Flood Genome, researchers assessed flood data accumulated over the last half-century. Their objective was to determine which areas are more prone to flooding and how susceptibility could change in response to population growth.
Professor Dr. Ali Mostafavi spearheaded the research, identifying three primary characteristics of flood-prone areas: proximity to streams and rivers, height above sea level (elevation), and historical rainfall amounts.
Prior to the Flood Genome, though experts understood these features’ influence on flood susceptibility, they lacked a data-driven method to evaluate flood risk based on these features in an interpretive way.
As this ground-breaking project finalizes, disaster preparedness groups across the country are being introduced to this novel approach to flood prediction and management.