Classification and Prediction of Coral Reef Bleaching Severity through Machine Learning

This study developed a machine learning framework that uses environmental variables to predict the degree of coral reef bleaching, filling some important gaps in current predictions. Apply random forest models, XGBoost, and neural network models in projects involving variant forests and pH anomalies, as well as temporal data to capture complex ecological interactions. The system sample corrected the widespread imbalance in coral data. A comparative analysis shows that the Random Forest model achieved an accuracy of 80% with a micro-average Area Under the Receiver Operating Characteristic Curve (ROC) Curve of 0.9118. This research goes beyond the binary classification method, which allows for severity measurement and identifies significant environmental factors by analyzing resources. The research results have created a powerful data-intensive framework for coral conservation measures under climate pressure. Future work will combine satellite observations and computer visualization to achieve a more precise resolution, ultimately enabling more proactive and targeted intervention strategies for preserving vulnerable reef ecosystems.