Communications of the ACM
Hybrid Machine Learning Forecasts Lake Ecosystem Responses to Climate Change
Said biological oceanographer George Sugihara at the University of California, San Diego’s Scripps Institution of Oceanography, "Single-factor experiments, the hallmark of 20th century science where everything is held constant, can teach you a lot i
Credit: Benoit Tissu
An international team of researchers applied a hybrid empirical dynamic modeling (EDM) methodology to predict the effects of climate change and phosphorus pollution on Switzerland's Lake Geneva.
George Sugihara at the University of California, San Diego's Scripps Institution of Oceanography explained that EDM is a form of supervised machine learning that can help model the mechanisms of interconnected and independent ecosystems.
The hybrid model implies that increasing air temperature by 3 degrees Celsius (5.4 degrees Fahrenheit) would impact Lake Geneva's water quality as much as the phosphorus contamination of the past 100 years has.
From UC San Diego Scripps Institution of Oceanography
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