Prediction of sediment concentration based on the MEEMD-ARIMA model in the lower Yellow River

Abstract

Abstract There are many factors influencing the evolution of sediment concentration, and it is difficult to determine and extract, which brings great difficulties to the high-precision prediction of sediment concentration. Accurate prediction of annual sediment concentration in the lower Yellow River can provide a theoretical basis for flood control and disaster reduction and rational utilization of water and soil resources in the lower Yellow River. For the defects of pseudo-components in data decomposition of Complementary EEMD, the Modified EEMD (MEEMD) method proposed in this paper has the advantage of eliminating pseudo components of IMF and reducing non-stationarity of sediment bearing sequences. Then, combined with the Autoregressive Integrated Moving Average (ARIMA) model with strong approximation ability to the stationary sequence, the MEEMD-ARIMA model for predicting the annual sediment concentration in the lower Yellow River was constructed. Through fitting and predicting the annual sediment concentration in Gaocun Station, it is shown that the model not only considers the evolution of sediment concentration in various frequency domains, but also solves the problem that the ARIMA model requires sequence to be stable, the relative error of prediction is within ±6%, and the prediction accuracy is high, thus providing a new method for the prediction of sediment concentration.