  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.
