Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application
This work addresses a practical issue in real-world water level forecasting for hydrology applications, but it is incremental as it improves upon existing sampling techniques rather than introducing a new paradigm.
The paper tackles the problem of avoiding future data leakage in decomposition-based hydrological forecasting by introducing a Fully Stepwise Decomposition-Based (FSDB) sampling technique, resulting in Nash-Sutcliffe Efficiency (NSE) coefficient increases of up to 28.8% for VMD-based models and up to 3.2% for SSA-based models compared to existing methods.
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.