A group of students, guided by climate science and environmental engineering professors, will use deep learning models to enhance flash flood predictions in the Southeastern United States. They will study extreme weather events that contribute to flooding and learn to identify these events using satellite and radar imagery. By applying their findings, the students will develop and refine deep learning models to improve flash flood risk predictions. Their work aims to create a more cost-efficient and accurate method for forecasting short-term heavy rainfall events.
Project Lead: Wenhong Li
Project Manager: Yan Pan, NSOE
