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HomeNewsML-based approach forecasts lake ecosystem's response to phosphorus pollution

ML-based approach forecasts lake ecosystem’s response to phosphorus pollution

The machine learning-based approach leads to a more actionable description of the biogeochemical and ecological processes that sustain water quality.

George Sugihara and his four international colleagues have discovered the machine learning-based empirical dynamic modeling (EDM) approach to forecast and manage Lake Geneva’s ecological response to the threat of phosphorus pollution. Sugihara is a biological oceanographer at Scripps Institution of Oceanography.

Phosphorus inputs from detergents and fertilizers have degraded the water quality of Switzerland’s Lake Geneva throughout the middle of the 20th century. This led the officials to take action to remediate pollution in the 1970s.

The authors explain that their machine learning-based approach leads to substantially better predictions and a more actionable description of the biogeochemical and ecological processes that sustain water quality.

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The hybrid model suggests that the impact of raised air temperature by 3°C (5.4°F) on water quality would be the same as the phosphorus pollution of the previous century. It also implies that the best management practices may no longer involve single control lever approaches like reducing phosphorus inputs alone.

The team of researchers also includes Damien Bouffard of the Swiss Federal Institute of Aquatic Sciences and Technology. The new hybrid empirical dynamic modeling (EDM) approach was published in the journal Proceedings of the National Academy of Sciences.

EDM can also help as a supervised machine learning tool, a way for computers to learn patterns and educate researchers about the mechanisms involved in data.

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Sahil Pawar
Sahil Pawar
I am a graduate with a bachelor's degree in statistics, mathematics, and physics. I have been working as a content writer for almost 3 years and have written for a plethora of domains. Besides, I have a vested interest in fashion and music.


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