A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue. To study ocean currents, scientists release GPS-tagged buoys in the ocean and record their velocities to reconstruct th
Computer scientists at MIT joined forces with oceanographers to develop a machine-learning model that incorporates knowledge from fluid dynamics to generate more accurate predictions about the velocities of ocean currents. This figure shows drifting buoy trajectories in the Gulf of Mexico superimposed on surface currents. The red dots mark the buoys’ positions on March 9, 2016, and the tails are 14 days long.
The researchers developed a new model that incorporates knowledge from fluid dynamics to better reflect the physics at work in ocean currents. They show that their method, which only requires a small amount of additional computational expense, is more accurate at predicting currents and identifying divergences than the traditional model.
Broderick’s co-authors include lead author Renato Berlinghieri, an electrical engineering and computer science graduate student; Brian L. Trippe, a postdoc at; David R. Burt and Ryan Giordano, MIT postdocs; Kaushik Srinivasan, an assistant researcher in atmospheric and ocean sciences at the University of California at Los Angeles; Tamay Özgökmen, professor in the Department of Ocean Sciences at the University of Miami; and Junfei Xia, a graduate student at the University of Miami.
“We were thinking we could address these problems with a model that incorporates the physics,” she says. This new model utilizes the same data as the old model. And while their method can be more computationally intensive, the researchers show that the additional cost is relatively small.They evaluated the new model using synthetic and real ocean buoy data. Because the synthetic data were fabricated by the researchers, they could compare the model’s predictions to ground-truth currents and divergences.
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