Abstract. Streamflow forecasting is a crucial component in the
management and control of water resources. Decomposition-based approaches
have particularly demonstrated improved forecasting performance. However,
direct decomposition of entire streamflow data with calibration and
validation subsets is not practical for signal component prediction. This
impracticality is due to the fact that the calibration process uses some
validation information that is not available in practical streamflow forecasting. Unfortunately, independent decomposition of calibration and
validation sets leads to undesirable boundary effects and less accurate forecasting. To alleviate such boundary effects and improve the forecasting
performance in basins lacking meteorological observations, we propose a
two-stage decomposition prediction (TSDP) framework. We realize this
framework using variational mode decomposition (VMD) and support vector
regression (SVR) and refer to this realization as VMD-SVR. We demonstrate experimentally the effectiveness, efficiency and accuracy of the TSDP
framework and its VMD-SVR realization in terms of the boundary effect
reduction, computational cost, and overfitting, in addition to decomposition and forecasting outcomes for different lead times. Specifically, four
comparative experiments were conducted based on the ensemble empirical mode
decomposition (EEMD), singular spectrum analysis (SSA), discrete wavelet
transform (DWT), boundary-corrected maximal overlap discrete wavelet
transform (BCMODWT), autoregressive integrated moving average (ARIMA), SVR,
backpropagation neural network (BPNN) and long short-term memory (LSTM). The
TSDP framework was also compared with the wavelet data-driven forecasting
framework (WDDFF). Results of experiments on monthly runoff data collected
from three stations at the Wei River show the superiority of the VMD-SVR
model compared to benchmark models.
Two-stage variational mode decomposition and support vector regression for streamflow forecasting
