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Component-based Reconstruction Prediction of Runoff at Multi-time Scales in the Source Area of the Yellow River Based on the ARMA Model

Author

Listed:
  • Jinping Zhang

    (Zhengzhou University
    Zhengzhou University)

  • Honglin Xiao

    (Zhengzhou University)

  • Hongyuan Fang

    (Zhengzhou University)

Abstract

Improving the accuracy of hydrological prediction for long-term annual runoff series is important for water resources management and planning. In this study, the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the runoff time series of the Tangnaihai hydrological station in the source area of the Yellow River (SAYR) from 1960 to 2017. The component reconstructions are evaluated with the Nash–Sutcliffe efficiency (NSE) coefficient, and the autoregressive moving average (ARMA) model is used to simulate and predict the runoff components at multi-time scales, which are also evaluated with the NSE coefficient. The results show that the NSE coefficient of the component simulations and predictions by ARMA are relatively high. Moreover, the NSE coefficients increase in step with the fluctuation period of the runoff component. For the runoff component-based reconstruction models at multi-time scales, the mean relative errors of simulation and prediction are low at 8.25% and 8.78%, respectively. In addition, the high-frequency components play an important role in the modal reconstruction as well as the simulation and prediction. Thus, focusing on the high-frequency components can improve the overall accuracy of runoff prediction based on the ARMA model at multi-time scales.

Suggested Citation

  • Jinping Zhang & Honglin Xiao & Hongyuan Fang, 2022. "Component-based Reconstruction Prediction of Runoff at Multi-time Scales in the Source Area of the Yellow River Based on the ARMA Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 433-448, January.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:1:d:10.1007_s11269-021-03035-7
    DOI: 10.1007/s11269-021-03035-7
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    References listed on IDEAS

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    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
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    Cited by:

    1. Binglin Li & Hao Xu & Yufeng Lian & Pai Li & Yong Shao & Chunyu Tan, 2023. "An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
    2. Wei Li & Xiaosheng Wang & Shujiang Pang & Haiying Guo, 2022. "A Runoff Prediction Model Based on Nonhomogeneous Markov Chain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1431-1442, March.
    3. Xianqi Zhang & Wenbao Qiao & Yaohui Lu & Jiafeng Huang & Yimeng Xiao, 2023. "Quantitative Analysis of the Influence of the Xiaolangdi Reservoir on Water and Sediment in the Middle and Lower Reaches of the Yellow River," IJERPH, MDPI, vol. 20(5), pages 1-18, February.

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