Designing a New Artificial Neural Network for Harmful Algal Blooms Prediction: A Case Study of Midmar Dam

Predicting algal proliferation in freshwater systems is crucial for effective water quality management and ecological sustainability. This study proposes a novel data-driven framework that integrates correlation-based feature ranking with a concatenation-enhanced artificial neural network (ANN) architecture to improve algae prediction accuracy. The analysis was conducted through a systematic evaluation of parameter relationships, employing Pearson’s correlation coefficient and standardized coefficients (Beta) to determine feature importance. Based on the magnitude of these coefficients, the input variables were progressively grouped into six feature sets, enabling a comparative assessment of predictive performance. The ANN models were trained and validated using root mean squared error (RMSE), mean absolute error (MAE) and Normalized Nash–Sutcliffe Efficiency (NNSE) as evaluation metrics. The results demonstrate that the fourth feature set, including chlorophyll-a, temperature, dissolved oxygen, total dissolved solids, and ammonia (NH3), identified through combined Pearson and Beta analysis, achieved the lowest prediction errors and superior generalization performance. These findings highlight the effectiveness of feature selection guided by correlation and standardized coefficients in enhancing ANN performance for algae prediction. The proposed framework offers valuable insights for improving the predictive modeling of algal dynamics, thereby supporting proactive water quality monitoring and the sustainable management of aquatic ecosystems.