The prediction of annual runoff in the Lower Yellow River can provide an important theoretical basis for effective reservoir management, flood control and disaster reduction, river and beach management, rational utilization of regional water and sediment resources. To solve this problem and improve the prediction accuracy, permutation entropy (PE) was used to extract the pseudo-components of modified ensemble empirical mode decomposition (MEEMD) to decompose time series to reduce the non-stationarity of time series. However, the pseudo-component was disordered and difficult to predict, therefore, the pseudo-component was decomposed by ensemble empirical mode decomposition (EEMD). Then, intrinsic mode functions (IMFs) and trend were predicted by autoregressive integrated moving average (ARIMA) which has strong ability of approximation to stationary series. A new coupling model based on MEEMD-ARIMA was constructed and applied to runoff prediction in the Lower Yellow River. The results showed that the model had higher accuracy and was superior to the CEEMD-ARIMA model or EEMD-ARIMA model. Therefore, it can provide a new idea and method for annual runoff prediction.

黄河下游年径流量的预测可为有效的水库管理，防洪减灾，河滩管理，合理利用区域水沙资源提供重要的理论基础。为了解决该问题并提高预测精度，使用置换熵（PE）提取改进的集成经验模式分解（MEEMD）的伪分量，以分解时间序列，以减少时间序列的非平稳性。但是，伪分量无序且难以预测，因此，通过整体经验模态分解（EEMD）将伪分量分解。然后，固有模式函数（IMF）和趋势是通过自回归积分移动平均（ARIMA）预测的，该函数具有很强的逼近平稳序列的能力。建立了基于MEEMD-ARIMA的新耦合模型，并将其应用于黄河下游径流预测。结果表明，该模型具有较高的准确性，优于CEEMD-ARIMA模型或EEMD-ARIMA模型。因此，可以为年径流量预报提供新的思路和方法。