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Monthly Runoff Prediction Model and Its Application Based on GPR with Physically Composite Kernel
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Na SUN1a, Nan ZHANG1b, Shuai ZHANG2, Tian PENG1a, Jian-zhong ZHOU3, Hai-rong ZHANG4
Water Resources and Power | 2023, 41(4) : 39 - 43
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Water Resources and Power | 2023, 41(4): 39-43
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
Monthly Runoff Prediction Model and Its Application Based on GPR with Physically Composite Kernel
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Na SUN1a, Nan ZHANG1b, Shuai ZHANG2, Tian PENG1a, Jian-zhong ZHOU3, Hai-rong ZHANG4
Affiliations
  • 1a.Faculty of Automation, Huaiyin Institute of Technology, Huaian 223003, China
  • 1b.Faculty of Mechanical and Material Engineering, Huaiyin Institute of Technology, Huaian 223003, China
  • 2.Xi’an Shufeng Technological Information Ltd., Xi’an 710054, China
  • 3.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 4.China Yangtze Power Co., Ltd., Yichang 443133, China
Published: 2023-04-25 doi: 10.20040/j.cnki.1000-7709.2023.20221932
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In light of the difficulty of traditional single runoff prediction models to describe future variation in runoff, a monthly runoff prediction model named AVMD-GPR-CK based on adaptive variational modal decomposition (AVMD) and Gaussian process regression (GPR-CK) with physically composite kernel was proposed. In the proposed model, the runoff series was decomposed into several subseries using AVMD. Then subseries were separately modeled according to their own characteristics, and the final prediction result was the superposition of the subsequence prediction results. The AVMD-GPR-CK was applied to forecast the future 1-12 months runoff at Xiangjiaba station in the Jinsha River basin. The results show that the deterministic coefficient of the AVMD-GPR-CK model is greater than 0.94, and the mean absolute percentage error (MMAPE) is within ±17% for all leading times, and the MMAPE is inside ±10% for leading times within 10 months. Furthermore, the accuracy of the AVMD-GPR-CK is significantly better than those of the commonly used BP, GRNN, RBF, and RELM models.

monthly runoff forecasting  /  variational mode decomposition  /  Gaussian process regression  /  composite kernel function
Na SUN, Nan ZHANG, Shuai ZHANG, Tian PENG, Jian-zhong ZHOU, Hai-rong ZHANG. Monthly Runoff Prediction Model and Its Application Based on GPR with Physically Composite Kernel[J]. Water Resources and Power, 2023 , 41 (4) : 39 -43 . DOI: 10.20040/j.cnki.1000-7709.2023.20221932
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Year 2023 volume 41 Issue 4
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20221932
  • Receive Date：2022-09-17
  • Online Date：2026-01-27
  • Published：2023-04-25
Article Data
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History
  • Received：2022-09-17
  • Revised：2022-10-19
Funding
Affiliations
    1a.Faculty of Automation, Huaiyin Institute of Technology, Huaian 223003, China
    1b.Faculty of Mechanical and Material Engineering, Huaiyin Institute of Technology, Huaian 223003, China
    2.Xi’an Shufeng Technological Information Ltd., Xi’an 710054, China
    3.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    4.China Yangtze Power Co., Ltd., Yichang 443133, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20221932
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