UT San Antonio Researcher Develops Self-Powered Smart Flood Warning System
Researchers at The University of Texas at San Antonio have developed a self-sustaining, artificial intelligence-powered flood warning system designed to detect dangerous water accumulation at street level. Led by Dr. Chen Pan, assistant professor of electrical engineering, the team engineered a field-ready prototype that integrates solar energy harvesting, multi-sensor environmental tracking, long-range wireless radios, and on-device machine learning. This system is designed to operate off-grid, generating its own power and evaluating flood risk locally without relying on external electricity or network lines. The prototype combines temperature, humidity, light, and precipitation sensing with four optical water-level sensors, achieving greater reliability through multi-modal sensing. The system utilizes TinyML to run machine learning algorithms directly on small, low-power microcontrollers, ensuring warnings are sent even if cell towers or internet connections fail during a storm.
