Machine learning improves oxygen level predictions in Eckernförde Bay using diverse ocean models

Even weak ocean models can offer valuable insights for environmental forecasts, a new study reveals. Researchers used machine learning to combine results from various models, including those with poor performance, to improve predictions of oxygen levels in Eckernförde Bay. This approach, published June 8, suggests that model diversity, not just high-performing models, can enhance forecasting accuracy for marine ecosystems.