基于 VMD-TCN-GRU 模型的水质预测研究.

In order to fully excavate the variation characteristics of water quality data in short-term shocks and improve the accuracy of prediction model, we proposed a VMD-TCN-GRU water quality prediction model based on Variational Mode Decomposition (VMD), Temporal Convolutional Network (TCN), and Gated Recurrent Unit (GRU). The VMD-TCN-GRU model was applied to predict the permanganate index at the outlet of Fenhe River Reservoir. Comparative analysis with commonly used models in water quality prediction, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM), TCN, and Convolutional Neural Network and Long Short-Term Memary (CNN-LSTM), demonstrates that the VMD-TCN-GRU model excels in extracting features during short-term oscillations in water quality data, identifying actual patterns of variation, facilitating comprehensive model learning, and thereby improving prediction accuracy. The VMD- TCN-GRU model achieves a Mean Absolute Error (MAE) of 0.0553, Root Mean Square Error (RMSE) of 0.0717, and a determination coefficient R² of 0.9351, indicating high predictive accuracy and strong generalization capabilities. This model can be effectively applied to water quality prediction tasks in the Fenhe River. [ABSTRACT FROM AUTHOR]