Smart and Self-Sustaining Early Warning Systems for Coastal Flooding

Summary

Texas A&M University - Corpus Christi will develop smart and self-sustaining early-warning systems for coastal flooding with state-of-the-art low-power Tiny Machine Learning (TinyML) and energy harvesting solutions. The project will embed sensors, Tiny ML, and connectivity into energy-harvesting-powered devices for sustainable, accurate, and real-time monitoring, prediction, and pre-warning of flooding paths and risk levels.