This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: When sudden rainstorms roll across Texas, flooding can erupt in a matter of minutes. From coastal bends facing storm surges to low-water crossings in San Antonio, flood risks often spike block by block long before regional alerts sound.
That means some people could have ankle-deep water in their yards by the time a flash flood warning sounds on their phones. But a better monitoring system could get the word out more swiftly—and more accurately. Researchers at The University of Texas at San Antonio have developed a self-sustaining, artificial intelligence-powered flood warning system designed to spot dangerous water accumulation at street level.
Led by Chen Pan, Ph.D., assistant professor of electrical engineering in the Margie and Bill Klesse College of Engineering and Integrated Design, the team engineered a field-ready prototype that combines solar energy harvesting, multisensor environmental tracking, long-range wireless radios and on-device machine learning. "In many rural areas or coastal communities, power infrastructure can fail right when severe weather strikes," said Pan, who directs the RISE Lab at UT San Antonio. "Our system is an off-grid solution.
It generates its own power, evaluates flood risk locally right on the device and sends timely warnings without needing external electricity or expensive network lines." The team includes Mimi Xie, assistant professor of computer science in the UT San Antonio College of AI, Cyber and Computing, as well as Texas A&M University-Corpus Christi collaborators Hua Zhang, professor of engineering, and Wenlu Wang, assistant professor of computer science. Texas holds a well-earned reputation for extreme flood events. While regional weather models and satellite imaging offer essential big-picture forecasts, they can miss hyperlocal flash floods.
A drainage channel behind a neighborhood, an isolated rural dip or a campus access road might submerge rapidly while surrounding areas remain dry. Existing commercial flood-monitoring stations also come with drawbacks, from prohibitively high costs to reliance on grid power or frequent battery replacements, leaving them vulnerable during multiday storms or power outages. Pan's team set out to engineer an inexpensive, all-in-one alternative.
The resulting prototype combines temperature, humidity, light and precipitation sensing with four optical water-level sensors mounted at varying heights. By analyzing multiple environmental factors simultaneously—an approach known as multimodal sensing—the system achieves far greater reliability than traditional single-metric gauges. One of the system's standout features is its computing power.
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