To fully explore the short-term fluctuation characteristics of water quality monitoring data and improve the accuracy of water quality prediction models, this study proposes a hybrid water quality prediction model based on the Runge–Kutta optimization algorithm, ensemble empirical mode decomposition (EEMD), Temporal Convolutional Network (TCN), and Bidirectional Long Short-Term Memory (BiLSTM) network. The optimized EEMD-TCN-BiLSTM model was applied to predict the permanganate index at the Sandao Section, and its prediction performance was compared with five mainstream models widely used in environmental science research, namely Bidirectional Long Short-Term Memory (BiLSTM) network, Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) network, extreme gradient boosting (XGBoost), and Temporal Convolutional Network (TCN). The comparison results show that the proposed model can extract the characteristic information of short-term fluctuations in water quality data more effectively and significantly improve the accuracy of water quality prediction. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R2) of the model reach 0.08288, 0.13152, and 0.95084, respectively, indicating reduced error indices and significantly improved fitting performance. The proposed model has superior prediction performance, higher prediction accuracy, and stronger generalization ability, which can provide scientific and quantitative technical support for real-time water quality monitoring, pollution risk early warning, and refined water environment management. Meanwhile, this model offers an integrated scientific approach for the sustainable development and utilization of water resources, and provides technical support for addressing water pollution and environmental sanitation, one of the core global sustainable development challenges.
Wang et al. (Fri,) studied this question.