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What is Ensemble ML for Algal Bloom Forecasting about?

This study presents an ensemble machine learning approach combining Gradient Boosting models (XGBoost, LightGBM, CatBoost) and an attention-based CNN-LSTM model for predicting harmful algal blooms (HABs). The models were optimized using Bayesian techniques and evaluated for their predictive performance, highlighting the potential of machine learning in improving HAB forecasting. The research emphasizes the importance of integrating advanced technologies for effective management of HABs, which pose significa

Author

bhanuprasadg66

Language

EN