2 days ago
Researchers develop game-changing tool to predict deadly natural disasters: 'Of paramount importance'
A recent study published in the Water Resources Research journal discussed using a new deep learning framework — known as Long Short-Term Memory Station Approximated Models, or LSTM-SAM — to predict the state of water levels during extreme weather events, in order to better forewarn and evacuate nearby civilians.
With Atlantic hurricane seasons lending themselves to more frequent and intense storm surges over the past decade, the impact of today's hurricanes could prove devastating for individuals and homes that aren't prepared. In particular, the coastal flooding associated with many hurricanes in the Southern U.S. has a history of endangering lives, buildings, and ecosystems.
Led by environmental engineering Ph.D. candidate Samuel Daramola, the researchers used a "transfer learning" technique to quickly and accurately make predictions with LSTM-SAM. While conventional storm prediction models rely on large bodies of weather and ocean data that are inefficient and expensive to assemble, LSTM-SAM estimates flood levels based on broader flood patterns recorded in the past.
One unique appeal of LSTM-SAM, per news, is the fact that accurate, high-efficiency storm predictions no longer need to be limited geographically to regions that have access to powerful data-processing facilities. Since LSTM-SAM bases its predictions on storm-flood patterns as a whole, the technology isn't locale-specific and can be applied to regions with minimal prior storm data.
"Other studies have relied on repetitive patterns in the training data," Daramola told "Our approach is different. We highlight extreme changes in water levels during training, which helps the model better recognize important patterns and perform more reliably in those areas."
More Atlantic hurricanes than ever are making landfall, which means the devastation wreaked by these storms cannot be understated. In fact, according to a 2023 report by the Front Page, rainfall flooding was responsible for more than half of the casualties caused by tropical cyclones.
"The need for reliable flood prediction frameworks is of paramount importance," continued. "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."
While we can't prevent hurricanes altogether, cutting-edge predictive innovations can help minimize the safety risks and allow residents time to plan for an evacuation. Meanwhile, since planet-warming carbon pollution considerably supercharges seasonal storms, we can take small steps to reduce our unfriendly contributions, such as installing home solar panels, repurposing household waste, and switching to an electric vehicle.
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