New Hybrid Machine Learning Forecasts Lake Ecosystem Responses to Climate Change

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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.

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, publishes its new hybrid empirical dynamic modeling (EDM) approach on June 20 in the journal Proceedings of the National Academy of Sciences.

Read more at: University of California - San Diego

Lake Geneva (Photo Cedit: Benoit Tissu)