Moving Beyond Temperature Metrics in Coral Bleaching Prediction Using Interpretable Machine Learning

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The model predicted coral bleaching intensities with 80% accuracy, highlighting the importance of multiple predictors.
Accumulated heat stress emerged as the strongest predictor, but not the only factor influencing bleaching outcomes.
The research employed an ordinal random forest model to analyze the effects of 19 environmental predictors on coral health.
Integrating diverse environmental factors may improve assessments of coral vulnerability and aid conservation efforts.

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Machine learning predicts coral bleaching risk in the Great Barrier Reef, suggesting new factors influence outcomes.

Cheung et al. (2025) studied this question.

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