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.

翻译：在实际水文时间序列预测中（例如水位预测），各类时变非平稳信号需要被适当预处理。分解方法是处理此类预问题的优选方案并被广泛采用。然而，采用不当采样技术的分解方法可能引入实际应用中不可获取的未来数据，导致基于分解的预测模型产生偏差。本研究设计了一种新型全逐步分解（FSDB）采样技术，专为基于分解的预测模型开发，严格避免了未来信息的引入。该采样技术结合变分模态分解（VMD）和奇异谱分析（SSA）等分解方法，应用于中国涡阳和巢湖流域三个不同站点的水位时间序列预测。采用FSDB采样技术的VMD混合模型结果显示，与当前最先进的采样技术相比，三个站点的纳什效率系数（NSE）分别提升了6.4%、28.8%和7.0%。与此同时，基于SSA的系列实验中，NSE分别提升了3.2%、3.1%和1.1%。研究结论表明，新型FSDB采样技术可有效提升基于分解的混合模型在实际水位时间序列预测中的性能。