Hybrid Models Improve Harmful Algal Bloom Forecasts in Chile
Global warming is making harmful algal blooms (HABs) more frequent and severe, while also complicating efforts to predict them. Traditional computer models often struggle to account for the wide range of algae species and the ways they interact under changing environmental conditions.
A research team led by Fumito Maruyama at Hiroshima University has found that combining multiple modeling approaches can significantly improve prediction accuracy. The team’s findings, published in Ecological Informatics, show that integrating physical, ecological, and machine learning models offers a more reliable...
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