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A Probability Model for Short-Term Streamflow Prediction Based on Multi-Resolution Data

Author

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  • Lili Wang

    (College of Physics and Electronic Engineering, Northwest Normal University
    Engineering Research Center of Gansu Province for Intelligent Information Technology and Application)

  • Zexia Li

    (College of Physics and Electronic Engineering, Northwest Normal University)

  • Fuqiang Ye

    (College of Physics and Electronic Engineering, Northwest Normal University)

  • Tongyang Liu

    (College of Physics and Electronic Engineering, Northwest Normal University)

Abstract

Reliable streamflow prediction is important for rational water resource planning. However, the strong nonlinearity and uncertainty of streamflow changes make accurate prediction challenging. Moreover, conventional streamflow prediction uses single-resolution data and provides deterministic prediction without uncertainty estimation, which leads to one-sidedness in data information extraction and risks in water resource decision-making. To improve streamflow prediction, this study proposes a probability prediction model integrating multi-resolution data for short-term streamflow prediction. In the proposed model, singular spectrum analysis (SSA) is utilized to process multi-resolution streamflow data to remove hidden noise. Then, support vector regression (SVR) is used for modelling, and grid search (GS) and cross-validation (CV) methods are employed to determine the optimal parameters of SVR. Finally, Gaussian process regression (GPR) is used for nonlinear fusion and probabilistic prediction. To verify the effectiveness of the proposed model, streamflow data from the Pingchuan bridge and Gaoya station with two different resolutions are collected, and several relevant models and indices are used for comparative analysis and comprehensive evaluation. The experimental results show that the proposed model significantly outperforms the relevant compared models, indicating that the proposed model effectively reduces the influence of interference signals on the modelling and fully utilizes the feature information provided by different resolution data to improve streamflow prediction. The results also confirm the superiority of integrating multi-resolution data over using single-resolution data in improving streamflow prediction. Moreover, the proposed model provides reliable uncertainty estimation in addition to providing accurate point prediction, which is helpful for water resource scheduling and decision-making. Therefore, the proposed model is recommended as a reliable method for streamflow prediction.

Suggested Citation

  • Lili Wang & Zexia Li & Fuqiang Ye & Tongyang Liu, 2023. "A Probability Model for Short-Term Streamflow Prediction Based on Multi-Resolution Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(14), pages 5601-5618, November.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:14:d:10.1007_s11269-023-03620-y
    DOI: 10.1007/s11269-023-03620-y
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    References listed on IDEAS

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    1. Lili Wang & Yanlong Guo & Manhong Fan, 2022. "Improving Annual Streamflow Prediction by Extracting Information from High-frequency Components of Streamflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4535-4555, September.
    2. Parisa Noorbeh & Abbas Roozbahani & Hamid Kardan Moghaddam, 2020. "Annual and Monthly Dam Inflow Prediction Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2933-2951, July.
    3. Saeed Mozaffari & Saman Javadi & Hamid Kardan Moghaddam & Timothy O. Randhir, 2022. "Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1955-1972, April.
    4. Ying Wang & Bo Feng & Qing-Song Hua & Li Sun, 2021. "Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    5. Hui Hu & Jianfeng Zhang & Tao Li, 2021. "A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5119-5138, December.
    6. Shangfu Wei & Xiaoqing Bai, 2022. "Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network," Energies, MDPI, vol. 15(5), pages 1-21, February.
    7. Lifeng Yuan & Kenneth J Forshay, 2021. "Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-25, April.
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