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.

影响泥沙浓度变化的因素很多，难以确定和提取，给高精度泥沙浓度预测带来很大困难。黄河下游年沉积物浓度的准确预测，可为防洪减灾，合理利用黄河下游水土资源提供理论依据。针对互补EEMD数据分解中伪分量的缺陷，本文提出的改进的EEMD（MEEMD）方法具有消除IMF伪分量，减少含沙序列非平稳性的优点。然后，结合对平稳序列具有较强逼近能力的自回归综合移动平均（ARIMA）模型，建立了预测黄河下游年沉积物浓度的MEEMD-ARIMA模型。通过拟合和预测高村站的年泥沙浓度，表明该模型不仅考虑了各频域泥沙浓度的变化，而且解决了ARIMA模型要求序列稳定，相对误差较大的问题。预测值在±6％以内，预测精度高，为沉积物浓度的预测提供了一种新方法。