Suspended sediment concentration forecast based on CEEMDAN-GRU model

Reasonable prediction of suspended sediment concentration is of great significance for river regulation and water conservancy project scheduling in upper and middle reaches. In order to solve the influence of nonlinear and non-stationary characteristics of sediment sequences on the prediction results and improve the prediction accuracy, a prediction model of sediment based on CEEMDAN-GRU was constructed. The monthly suspended sediment concentration data of Huayuankou hydrological station from 1960 to 2014 were ‘decomposed-predicted-reconstructed’ and compared with single gated recurrent unit (GRU), support vector machine (SVM) and long short-term memory (LSTM) model. The results reveal that the CEEMDAN-GRU coupling model has provided a superior alternative to the single model and its determination coefficients (DC) of the training set and testing set are greater than 0.71, qualified rate (QR) reaches up to 81%, average absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) are 1.2841, 0.9675 and 4.3560 respectively. It is proved that the CEEMDAN-GRU model has a better performance and can be used in the mid- and long-term prediction of non-linear and non-stationary suspended sediment concentration series.