This paper proposes a new hybrid deep neural network model for river water quality prediction, which is integrated with Savitaky-Golay (SG) filter, STL time series decomposition method, Self-attention mechanism, and Temporal Convolutional Network (TCN). The SG filter removes the noise in the time series data of river water quality, and the STL technology decomposes the time series data into trend, seasonal and residual series. The decomposed trend series and residual series are input into the model combining the Self-attention mechanism and TCN respectively for training and prediction. Experiments conducted using opensource and private water quality data show that the proposed model achieves the best prediction results in the water quality data of two different rivers.
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