FORECASTING PHOSPHORUS POLLUTION IN LAKE GENEVA WITH HYBRID MACHINE LEARNING
In the 70s , phosphorus inputs from detergents and fertilisers degraded the water quality of Switzerland ' s Lake Geneva , encouraging officials to take action to remediate the pollution .
To combat it , George 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 , as well as the added effects of climate change .
" Nature is much more interconnected and interdependent than scientists would often like to think ," said George Sugihara , a biological oceanographer at UC San Diego ' s Scripps Institution of Oceanography .
Recently , the team published its new hybrid empirical dynamic modelling ( EDM ) approach , which can help forecast phosphorus pollution in Lake Geneva as a form of supervised machine learning – a way for computers to learn patterns and teach researchers about the mechanisms behind the data .
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