Wednesday June 22, 2022
PhysOrg —
Throughout the middle of the 20th century, phosphorus inputs from detergents and fertilizers degraded the water quality of Switzerland’s Lake Geneva, spurring officials to take action to remediate pollution in the 1970s.
“The obvious remedy was to reverse the phosphorus loading, and this simple idea helped enormously, but it didn’t return the lake to its former state, and that’s the problem,” said George Sugihara, a biological oceanographer at UC San Diego’s Scripps Institution of Oceanography.
Sugihara, Boston University’s Ethan Deyle, and three international colleagues spent five years searching for a better way to forecast and manage Lake Geneva’s ecological response to the threat of phosphorus pollution, to which the effects of climate change must now be added. The team, including Damien Bouffard of the Swiss Federal Institute of Aquatic Sciences and Technology, published its new hybrid empirical dynamic modeling (EDM) approach on June 20 in the journal Proceedings of the National Academy of Sciences.
“Nature is much more interconnected and interdependent than scientists would often like to think,” said Sugihara, the McQuown Chair Professor of Natural Science at Scripps. EDM can help in this context as a form of supervised machine learning, a way for computers to learn patterns and teach researchers about the mechanisms behind the data.