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Various time variant non-stationary signals need to be pre-processed properlyin hydrological time series forecasting in real world, for example, predictionsof water level. Decomposition method is a good candidate and widely used insuch a pre-processing problem. However, decomposition methods with aninappropriate sampling technique may introduce future data which is notavailable in practical applications, and result in incorrectdecomposition-based forecasting models. In this work, a novel Fully StepwiseDecomposition-Based (FSDB) sampling technique is well designed for thedecomposition-based forecasting model, strictly avoiding introducing futureinformation. This sampling technique with decomposition methods, such asVariational Mode Decomposition (VMD) and Singular spectrum analysis (SSA), isapplied to predict water level time series in three different stations ofGuoyang and Chaohu basins in China. Results of VMD-based hybrid model usingFSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficientis increased by 6.4%, 28.8% and 7.0% in three stations respectively, comparedwith those obtained from the currently most advanced sampling technique. In themeantime, 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 samplingtechnique can be used to enhance the performance of decomposition-based hybridmodel in water level time series forecasting in real world.

Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application