School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
The Yellow River Basin Water Resources Efficient Utilization of the Provincial and Ministry Co-Built Collaborative Innovation Center, Zhengzhou, 450046, China.
Environ Sci Pollut Res Int. 2022 Jul;29(35):52806-52817. doi: 10.1007/s11356-022-19572-6. Epub 2022 Mar 10.
Runoff forecasting is essential for the reasonable use of regional water resources, flood prevention, and mitigation, as well as the development of ecological civilization. Runoff is influenced by the intersection of many factors, and the change process is extremely complex, showing significant stochasticity, nonlinearity, and heterogeneity, making traditional prediction models less adaptable. The Hodrick-Prescott filter (HP filter) is a well-established signal separation method. The traditional GM(1,1) model is capable of portraying the growth trend of the time series but cannot capture the periodic characteristics of the time series. Therefore, a novel coupled prediction model-HPF-GM(1,1) model is proposed in this study and applied to the runoff prediction of the Zhuzhou section of Xiangjiang River in Hunan Province. This model enables to separate seasonal factors from non-seasonal factors in the runoff time series, and introduce seasonal factors based on the traditional GM(1,1) model, which solves the challenge that the traditional GM(1,1) model is unable to predict seasonal time series. The results show that the HPF-GM(1,1) model has a mean relative error of 4.82%, a root mean square error of 7.44, and a Nash efficiency coefficient of 0.93, which is better than the traditional GM(1,1) model, the DGGM(1,1) model and the SGM(1,1) model of prediction accuracy. Obviously, the HP filter provides a new approach to data pre-processing of runoff series and the proposed HPF-GM(1,1)-coupled model extends new ideas for nonlinear runoff prediction.
径流量预测对于合理利用区域水资源、防洪减灾以及生态文明建设至关重要。径流量受到多种因素的交叉影响，其变化过程极其复杂，表现出显著的随机性、非线性和非均匀性，使得传统的预测模型适应性较差。Hodrick-Prescott 滤波器（HP 滤波器）是一种成熟的信号分离方法。传统的 GM(1,1)模型能够描述时间序列的增长趋势，但无法捕捉时间序列的周期性特征。因此，本研究提出了一种新的耦合预测模型——HPF-GM(1,1)模型，并将其应用于湖南省湘江株洲段的径流量预测。该模型能够将径流量时间序列中的季节性因素与非季节性因素分离，并基于传统的 GM(1,1)模型引入季节性因素，解决了传统 GM(1,1)模型无法预测季节性时间序列的问题。结果表明，HPF-GM(1,1)模型的平均相对误差为 4.82%，均方根误差为 7.44，纳什效率系数为 0.93，预测精度优于传统 GM(1,1)模型、DGGM(1,1)模型和 SGM(1,1)模型。显然，HP 滤波器为径流序列的数据预处理提供了一种新方法，所提出的 HPF-GM(1,1)耦合模型为非线性径流预测提供了新的思路。
