The 2025 hurricane season officially began on June 1, and, as of July, it is set to be more active than ever, with potentially devastating storms whose heavy rainfall and powerful storm surges cause dangerous coastal flooding.
Extreme water levels can be difficult to predict without complex, data-intensive computer models that areas with limited resources cannot support. Researchers have made a new attempt to assess the potential impact using a deep learning framework to predict the rise and fall of water levels during storms — even in places where tide gauges fail or data is scarce — through a technique known as “transfer learning.”
The framework, called Long Short-Term Memory Station Approximated Models (LSTM-SAM), offers faster and more affordable predictions that enable smarter decisions about when to evacuate, where to place emergency resources, and how to protect infrastructure when hurricanes approach. For emergency planners, local governments, and disaster response teams, it could be a game-changer — and could save lives.
Predicting when and where extreme water levels will strike — especially during compound floods, when multiple flooding sources, like rain and storm surge, combine to intensify flooding — is important for protecting vulnerable communities.
LSTM-SAM is a deep-learning framework that analyses patterns from past storms to predict water level rise during future storms. What makes this model especially useful is its ability to extrapolate from one geographic area’s data to make predictions for another locale that doesn’t have a lot of its own data. By borrowing knowledge and applying it locally, it makes accurate flood prediction more widely available.
The researchers tested LSTM-SAM at tide gauge stations along the Atlantic coast of the United States, a region frequently impacted by hurricanes and other major storms. They found that the model was able to accurately predict the onset, peak, and decline of storm-driven water levels. The model was even able to reconstruct water levels for tide-gauge stations damaged by hurricanes, such as the station in Sandy Hook, New Jersey, which failed during Hurricane Sandy in 2012.
Researchers plan on using the LSTM-SAM framework during the upcoming hurricane season, where they can test it as storms roll in nearly in real time. They’ve also made the code available in the GitHub repository of the CoRAL Lab, where scientists, emergency planners, and government leaders can download it for free. The program runs on a laptop in a matter of minutes and could be especially helpful for smaller towns or regions in developing countries where access to high-end computing tools or detailed environmental data is limited.
As the frequency of hurricane events and their socioeconomic impact is likely to increase in the future, the need for reliable flood prediction frameworks is of paramount importance. Advanced deep learning tools like LSTM-SAM could become essential in helping coastal communities prepare for the new normal, opening the door to smarter, faster, and more accessible flood predictions associated with tropical cyclones.
The study appears in the journal Water Resources Research, titled “Predicting the Evolution of Extreme Water Levels With Long Short-Term Memory Station-Based Approximated Models and Transfer Learning Techniques.”
