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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 20 Dec 2019

Submitted as: research article | 20 Dec 2019

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This preprint is currently under review for the journal HESS.

Two-stage Variational Mode Decomposition and Support Vector Regression for Streamflow Forecasting

Ganggang Zuo, Jungang Luo, Ni Wang, Yani Lian, and Xinxin He Ganggang Zuo et al.
  • State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China

Abstract. Streamflow forecasting is a crucial component in the management and control of water resources. Decomposition-based approaches have particularly demonstrated improved streamflow forecasting performance. However, it is not practical to firstly decompose the entire streamflow into several signal components and then divide the data samples of each component into training and validation sets for signal component prediction. This impracticality is due to the fact that some validation information, that is not available in practical streamflow forecasting, is used in that training process. Unfortunately, firstly dividing the entire streamflow into training and validation sets and then decomposing each set separately lead to undesirable boundary effects and complicated forecasting. Moreover, establishing a model for each signal component is quite laborious and summing the component predictions may lead to error accumulation. In addition, summing the decomposition results may sometimes lead to inaccurate reconstruction of the original streamflow. In order to address these shortcomings of decomposition-based models and improve the forecasting performance in basins lacking meteorological observations (e.g., precipitation and temperature), we propose a two-stage decomposition prediction (TSDP) framework, realize this framework using variational mode decomposition (VMD) and support vector machines (SVR), and refer to this realization as VMD-SVR. In the first stage of the TSDP framework, the entire streamflow data was divided into training and validation sets, each of which was then separately decomposed to avoid the influence of validation information on training. In the second stage, a single model for streamflow prediction was established using a set of mixed shuffled samples. This scheme saves the modelling time and reduces the influence of the boundary effects. We demonstrate experimentally the effectiveness, efficiency and reliability of the TSDP framework and its VMD-SVR realization in terms of the boundary effect reduction, decomposition performance, prediction outcomes, time consumption, overfitting, and forecasting capability for long leading times. Specifically, five comparative experiments were conducted based on the ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA), discrete wavelet transform (DWT) and SVR. The experimental results on monthly runoff collected from three stations at the Wei River show the superiority of the TSDP framework compared to benchmark models.

Ganggang Zuo et al.

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Ganggang Zuo et al.

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Short summary
A two-stage variational mode decomposition (VMD) and support vector regression (SVR), i.e., VMD-SVR, was designed to reduce the influence of boundary effect and improve forecasting performance and efficiency when forecasting monthly runoff. The research results indicate VMD-SVR significantly reduces the influence of boundary effect, saves modeling time and computation resources, barely overfits the training samples, forecasts monthly runoff reasonably well compared to the benchmark models.
A two-stage variational mode decomposition (VMD) and support vector regression (SVR), i.e.,...