Machine Learning Approaches for Harmful Algal Bloom Prediction: Advances, Challenges, and Future Directions

Harmful algal blooms (HABs) represent one of the most pressing environmental challenges of the 21st century, causing substantial economic losses estimated at $50 million annually in the United States alone, threatening public health through toxin exposure and disrupting aquatic ecosystems worldwide. The increasing frequency and intensity of HABs, exacerbated by climate change and anthropogenic nutrient loading, have driven urgent demand for accurate prediction systems. Machine learning (ML) has emerged as a transformative approach for HAB prediction, offering capabilities to capture complex nonlinear relationships between environmental drivers and bloom dynamics that traditional statistical and process-based models cannot adequately represent. This comprehensive review synthesizes findings from 168 peer-reviewed studies published between 1997 and 2025, examining the current state, and future directions of ML-based HAB prediction. We analyze the full spectrum of ML approaches, from traditional algorithms including random forest, support vector machines, and gradient boosting methods to advanced deep learning architectures such as long short-term memory (LSTM) networks, convolutional neural networks, and emerging transformer models. Our analysis reveals that deep learning approaches, particularly LSTM-based models, have achieved superior performance with median R2 values of 0.89 and accuracies exceeding 90% in many applications. Hybrid approaches combining process-based models with ML have demonstrated the highest predictive accuracy, with Nash–Sutcliffe efficiency values reaching 0.991. Despite significant advances, critical challenges remain, including limited model transferability across water bodies, data scarcity and class imbalance, insufficient adoption of explainable AI techniques (only 20% of studies), and the persistent gap between research models and operational early warning systems. We identify key research priorities including enhanced interpretability through SHAP and attention mechanisms, the integration of climate change projections, the development of transfer learning frameworks for data-limited systems, and the establishment of standardized benchmarking datasets. We further examine the application of ML approaches to total maximum daily load development and compliance, highlighting how predictive models can inform nutrient load allocations, support scenario analysis, and enhance implementation monitoring for HAB-impaired water bodies. This review provides researchers and practitioners with a comprehensive roadmap for advancing ML-based HAB prediction toward more accurate, interpretable, and operationally deployable systems.