Researchers use deep learning to predict flooding this hurricane season

A team led by Virginia Tech graduate student Samuel Daramola developed a deep learning model called LSTM-SAM that predicts extreme water levels from tropical cyclones more efficiently and accurately, especially in data-scarce coastal regions, to offer a faster, low-cost tool for flood forecasting.

A team led by Virginia Tech graduate student Samuel Daramola developed a deep learning model called LSTM-SAM that predicts extreme water levels from tropical cyclones more efficiently and accurately, especially in data-scarce coastal regions, to offer a faster, low-cost tool for flood forecasting.

Read more https://www.sciencedaily.com/releases/2025/06/250602154901.htm