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Short-term Prediction Method of Reservoir Downstream Water Level Under Complicated Hydraulic Influence

Author

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  • Jingwei Huang

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Hui Qin

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Yongchuan Zhang

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Dongkai Hou

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Sipeng Zhu

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

  • Pingan Ren

    (Huazhong University of Science and Technology
    Hubei Key Laboratory of Digital Valley Science and Technology)

Abstract

The downstream water level of a reservoir is influenced by its own discharge, changes in external hydraulic conditions, and the value of the previous period’s downstream water level, and is very sensitive to hourly changes. However, the influence mechanisms of this change and an accurate prediction method have yet to be investigated. In this study, the downstream water level of Xiangjiaba reservoir in China’s Jinsha river was used as a case study to analyze the impact of backwater effects caused by river rising during the flood season and the effect of sharp fluctuations caused by the peak regulation flow during the non-flood season. Moreover, an accurate prediction method at short-term two hourly scale is proposed. This study quantified the backwater effect caused by the rising tributaries of Hengjiang and Minjiang rivers. The random forest algorithm (RF) was used to downscale and rank multidimensional feature data, build different model factor sets, and build a downstream water level prediction model using five different methods. The results showed that the data mining model had the best fit and good prediction ability for the downstream water level of the Xiangjiaba reservoir under the influence of complicated hydraulic factors during the flood season, and can effectively control the fluctuation error during the peak regulation period. The research findings can be applied to other similar basins to improve the reservoir’s short-term refined operational levels.

Suggested Citation

  • Jingwei Huang & Hui Qin & Yongchuan Zhang & Dongkai Hou & Sipeng Zhu & Pingan Ren, 2023. "Short-term Prediction Method of Reservoir Downstream Water Level Under Complicated Hydraulic Influence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4475-4490, September.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:11:d:10.1007_s11269-023-03570-5
    DOI: 10.1007/s11269-023-03570-5
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    References listed on IDEAS

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    1. Tarcila Neves Generoso & Demetrius David Silva & Ricardo Santos Silva Amorim & Lineu Neiva Rodrigues & Erli Pinto Santos, 2022. "Methodology for Estimating Streamflow by Water Balance and Rating Curve Methods Based on Logistic Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4389-4402, September.
    2. Mehdi Jamei & Mumtaz Ali & Anurag Malik & Ramendra Prasad & Shahab Abdulla & Zaher Mundher Yaseen, 2022. "Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4637-4676, September.
    3. Mohamed Hamitouche & Jose-Luis Molina, 2022. "A Review of AI Methods for the Prediction of High-Flow Extremal Hydrology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3859-3876, August.
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    Cited by:

    1. Huang, Jingwei & Qin, Hui & Shen, Keyan & Yang, Yuqi & Jia, Benjun, 2024. "Study on hierarchical model of hydroelectric unit commitment based on similarity schedule and quadratic optimization approach," Energy, Elsevier, vol. 305(C).

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