Water quality deterioration poses a critical threat to ecological security and sustainable development, particularly in rapidly urbanizing regions. To enable proactive environmental management, this study develops a novel hybrid deep learning model, the NGO-CNN-GRU, for high-precision time-series water quality prediction in the Xijiang River Basin, China. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Gated Recurrent Unit (GRU) for temporal dependency modeling, with hyperparameters optimized via the Northern Goshawk Optimization (NGO) algorithm. Using historical water quality (pH, DO, CODMn, NH3-N, TP, TN) and meteorological data (precipitation, temperature, humidity) from 11 monitoring stations, the model achieved exceptional performance: test set R² > 0.986, MAE < 0.015, and RMSE < 0.018 for total nitrogen prediction (Xiaodong Station case study). Across all stations and indicators, it consistently outperformed baseline models (GRU, CNN-GRU), with average R² improvements of 12.3% and RMSE reductions up to 90% for NH3-N predictions. Spatiotemporal analysis further revealed significant pollution gradients correlated with anthropogenic activities in the Pearl River Delta. This work provides a robust tool for real-time water quality early warning systems and supports evidence-based river basin management.

Excessive total nitrogen (TN) in water bodies leads to eutrophication, algal blooms, and hypoxia, which pose significant risks to aquatic ecosystems and human health. Accurate real-time TN prediction is crucial for effective water quality management. This study presents an innovative approach that combines the distance correlation coefficient (DCC) for feature selection with a coupled Attention-Convolutional Neural Network-Bidirectional Long Short-Term Memory (At-CBiLSTM) model to predict TN concentrations in the Dongjiang River in China. A dataset of 28,922 time-series data points was collected from seven sampling sites along the Dongjiang River, spanning from November 2020 to February 2023. The DCC method identified conductivity, Permanganate Index (COD_Mn), and total phosphorus as the most significant predictors for TN levels. The At-CBiLSTM model, optimized with a time step of three, outperformed other models, including standalone Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Convolutional Neural Network LSTM (CNN-LSTM), and Attention-LSTM variants, achieving excellent performance with the following metrics: mean absolute error (MAE) = 0.032, mean squared error (MSE) = 0.005, mean absolute percentage error (MAPE) = 0.218, and root mean squared error (RMSE) = 0.045. Importantly, increasing the number of input features beyond three variables led to a decline in model accuracy, underscoring the importance of DCC-driven feature selection. The results highlight that combining DCC with deep learning models, particularly At-CBiLSTM, effectively captures nonlinear temporal dependencies and improves prediction accuracy. This approach provides a solid foundation for real-time water quality monitoring and can inform targeted pollution control strategies in river ecosystems.