A Decomposition-Based Deep Learning Model for Multivariate Water Quality Prediction

The extensive deployment of automatic water quality monitoring stations has generated substantial volumes of time-series data. Effectively utilizing these data is crucial for enhancing prediction accuracy. To address the limitations of existing models in capturing complex inter-indicator relationships and multi-scale temporal features, this paper proposes a hybrid prediction model integrating time series decomposition with deep learning techniques. Adopting a “decomposition–prediction–reconstruction” paradigm, the model first decomposes the raw time series into trend, seasonal, and residual components using STL (Seasonal–Trend decomposition using LOESS). For the trend component, an improved Graph Convolutional Network (GCN) is designed to explicitly model the spatial dependencies among different water quality indicators. For the seasonal component, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is employed for multi-scale signal analysis, followed by a coupled Long Short-Term Memory–Convolutional Neural Network (LSTM-CNN) unit to capture both long-term dependencies and local features. To validate the efficacy of the proposed model, experiments were conducted on three real-world water quality datasets from different watersheds. Experimental results demonstrate that the proposed model outperforms mainstream baseline models, including StemGCN, LSTM-CNN, CEEMDAN-LSTM-CNN, and Attention-CLX. Across the three datasets, the model consistently outperforms the best-performing baseline, achieving reductions in MAE ranging from 13.8% to 24.5% and up to a 45.3% reduction in RMSE on a single dataset, while the highest correlation coefficient between predicted and observed values reaches 0.855. These findings demonstrate that the proposed decomposition–integration framework effectively enhances the accuracy and stability of multivariate water quality prediction, offering a promising tool for supporting sustainable water resource management.