DOI: 10.5194/hess-24-5491-2020
论文题名: Two-stage variational mode decomposition and support vector regression for streamflow forecasting
作者: Zuo G. ; Luo J. ; Wang N. ; Lian Y. ; He X.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期: 11 起始页码: 5491
结束页码: 5518
语种: 英语
Scopus关键词: Autoregressive moving average model
; Backpropagation
; Calibration
; Discrete wavelet transforms
; Forecasting
; Long short-term memory
; Signal reconstruction
; Spectrum analysis
; Stream flow
; Water resources
; Wavelet decomposition
; Auto-regressive integrated moving average
; Back-propagation neural networks
; Calibration and validations
; Ensemble empirical mode decompositions (EEMD)
; Maximal overlap discrete wavelet transforms
; Meteorological observation
; Singular spectrum analysis
; Support vector regression (SVR)
; Support vector regression
; decomposition
; forecasting method
; streamflow
; support vector machine
; vector autoregression
英文摘要: 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 datadriven 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. © Author(s) 2020.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162544
Appears in Collections: 气候变化与战略
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作者单位: Zuo, G., State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi, 710048, China; Luo, J., State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi, 710048, China; Wang, N., State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi, 710048, China; Lian, Y., State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi, 710048, China; He, X., State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi, 710048, China
Recommended Citation:
Zuo G.,Luo J.,Wang N.,et al. Two-stage variational mode decomposition and support vector regression for streamflow forecasting[J]. Hydrology and Earth System Sciences,2020-01-01,24(11)