Water quality prediction in the Huaihe river basin using a hybrid deep learning model with adaptive unscented Kalman filter

Abstract

To address the problems of low model prediction accuracy caused by time-varying nonlinearity and susceptibility to random noise in water quality data, a water quality prediction model based on IGJO-TCN-BiGRU-MHSA has been proposed. First, the adaptive unscented Kalman filter is adopted to filter out random noise in the data, thereby improving data smoothness. Second, the bidirectional gated recurrent unit (BiGRU) is utilized as the benchmark model, capturing both forward and backward temporal features of the water quality data simultaneously. A temporal convolutional network (TCN) is adopted to extract local features from the data, enhancing the ability of the model to extract features at different time scales. Multi-head self-attention (MHSA) is adopted to capture key information in the data in parallel, thereby enhancing the multi-scale feature fusion ability of the model. Finally, the improved golden jackal optimization (IGJO) algorithm is utilized to optimize the L2 of the TCN, the learning rate and the neuron number of the BiGRU, and the key dimension of the MHSA, improving the accuracy of water quality prediction. Historical water quality data from the Huaihe River Basin were used as experimental samples. Compared with classical algorithms such as SVR, LSTM, GRU, BiGRU, and TCN-BiGRU-MHSA, the IGJO-TCN-BiGRU-MHSA prediction algorithm proposed in this study reduced the mean absolute error (MAE) by 39.18%, 34.15%, 31.15%, 27.85%, and 1.94%, reduced the root mean square error (RMSE) by 35.05%, 27.60%, 24.46%, 20.53%, and 3.06%, and improved the coefficient of determination (R2) by 5.34%, 3.39%, 2.89%, 2.21%, and 0.18%, respectively. The experimental results demonstrated that the prediction algorithm based on IGJO-TCN-BiGRU-MHSA significantly improved the water quality prediction accuracy.