Machine Learning-Based Early Warning of Algal Blooms: A Case Study of Key Environmental Factors in the Anzhaoxin River Basin

Algal blooms are a major risk to aquatic ecosystem health and potable water safety. Traditional statistical models often fail to accurately predict algal bloom dynamics due to their complexity. Machine learning, adept at managing high-dimensional and non-linear data, provides a superior predictive approach to this challenge. In this study, we employed support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN) models to predict the severity of algal blooms in the Anzhaoxin River Basin based on an algal density-based grading standard. The SVM model demonstrated the highest accuracy with training and test set accuracies of 0.96 and 0.92, highlighting its superiority in small-sample learning. The Shapley Additive Explanations (SHAP) technique was utilized to evaluate the contribution of environmental variables in various predictive models. The results show that TP is the most significant environmental factor affecting the algal bloom outbreak in Anzhaoxin River, and the phosphorus management strategy is more suitable for the management of the artificial water body in northeast China. This study contributes to exploring the potential application of machine learning models in diagnosing and predicting riverine ecological issues, providing valuable insights and support for the protection and management of aquatic ecosystems in the Anzhaoxin River Basin.