Articles | Volume 24, issue 11
https://doi.org/10.5194/hess-24-5491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-5491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Nov 2020
Research article |
| 23 Nov 2020
| 23 Nov 2020
Two-stage variational mode decomposition and support vector regression for streamflow forecasting
Ganggang Zuo
State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
Jungang Luo
CORRESPONDING AUTHOR
State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
Ni Wang
CORRESPONDING AUTHOR
State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
Yani Lian
State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
Xinxin He
State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
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Latest update: 02 Apr 2025
Short summary
A two-stage variational mode decomposition and support vector regression is designed to reduce the influence of boundary effects without removing or correcting boundary-affected decompositions. The proposed model significantly reduces the boundary effect consequences, saves modeling time and computation resources, barely overfits the calibration samples, and forecasts monthly runoff reasonably well compared to the benchmark models.
A two-stage variational mode decomposition and support vector regression is designed to reduce...
