Various time variant non-stationary signals need to be pre-processed properly in hydrological time series forecasting in real world, for example, predictions of water level. Decomposition method is a good candidate and widely used in such a pre-processing problem. However, decomposition methods with an inappropriate sampling technique may introduce future data which is not available in practical applications, and result in incorrect decomposition-based forecasting models. In this work, a novel Fully Stepwise Decomposition-Based (FSDB) sampling technique is well designed for the decomposition-based forecasting model, strictly avoiding introducing future information. This sampling technique with decomposition methods, such as Variational Mode Decomposition (VMD) and Singular spectrum analysis (SSA), is applied to predict water level time series in three different stations of Guoyang and Chaohu basins in China. Results of VMD-based hybrid model using FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient is increased by 6.4%, 28.8% and 7.0% in three stations respectively, compared with those obtained from the currently most advanced sampling technique. In the meantime, for series of SSA-based experiments, NSE is increased by 3.2%, 3.1% and 1.1% respectively. We conclude that the newly developed FSDB sampling technique can be used to enhance the performance of decomposition-based hybrid model in water level time series forecasting in real world.

在水文时间序列预测中，各种时间变异的非平稳信号需要进行适当的预处理，以避免引入未来信息。本文设计了一种新颖的完全逐步分解（FSDB）采样技术，应用于分解模型，以严格避免引入未来信息，并将其应用于中国国阳和巢湖流域三个不同站点的水位时间序列预测。结果显示，使用FSDB采样技术的VMD-based混合模型在三个站点的Nash-Sutcliffe Efficiency（NSE）系数分别提高了6.4％，28.8％和7.0％，与目前最先进的采样技术相比。对于SSA-based实验，NSE系数分别提高了3.2％，3.1％和1.1％。因此，新开发的FSDB采样技术可以提高水位时间序列预测中分解模型的性能。

一种实现新的全步骤分解式采样技术的混合水位预测模型在实际应用中的研究