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Application of improved seasonal GM(1,1) model based on HP filter for runoff prediction in Xiangjiang River

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Abstract

Runoff forecasting is essential for the reasonable use of regional water resources, flood prevention, and mitigation, as well as the development of ecological civilization. Runoff is influenced by the intersection of many factors, and the change process is extremely complex, showing significant stochasticity, nonlinearity, and heterogeneity, making traditional prediction models less adaptable. The Hodrick–Prescott filter (HP filter) is a well-established signal separation method. The traditional GM(1,1) model is capable of portraying the growth trend of the time series but cannot capture the periodic characteristics of the time series. Therefore, a novel coupled prediction model-HPF-GM(1,1) model is proposed in this study and applied to the runoff prediction of the Zhuzhou section of Xiangjiang River in Hunan Province. This model enables to separate seasonal factors from non-seasonal factors in the runoff time series, and introduce seasonal factors based on the traditional GM(1,1) model, which solves the challenge that the traditional GM(1,1) model is unable to predict seasonal time series. The results show that the HPF-GM(1,1) model has a mean relative error of 4.82%, a root mean square error of 7.44, and a Nash efficiency coefficient of 0.93, which is better than the traditional GM(1,1) model, the DGGM(1,1) model and the SGM(1,1) model of prediction accuracy. Obviously, the HP filter provides a new approach to data pre-processing of runoff series and the proposed HPF-GM(1,1)-coupled model extends new ideas for nonlinear runoff prediction.

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Datasets and other materials are available with the authors, and may be accessible at any time upon request.

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Funding

The authors wish to thank the Key Scientific Research Project of Colleges and Universities in Henan Province (CN) [grant number 17A570004] for the collection, analysis, and interpretation of data.

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Authors and Affiliations

  1. School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China

    Xianqi Zhang, Xilong Wu, Yimeng Xiao, Jingwen Shi, Yue Zhao & Minghui Zhang

  2. The Yellow River Basin Water Resources Efficient Utilization of the Provincial and Ministry Co-Built Collaborative Innovation Center, Zhengzhou, 450046, China

    Xianqi Zhang

  3. Henan Water Conservancy and Water Transportation Engineering Research Center, Zhengzhou, 450046, China

    Xianqi Zhang

Authors
  1. Xianqi Zhang
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  2. Xilong Wu
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  3. Yimeng Xiao
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  4. Jingwen Shi
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  5. Yue Zhao
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  6. Minghui Zhang
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Contributions

Zhang XQ: methodology, investigation, and writing—original draft preparation. Wu XL: conceptualization and writing review and editing. Xiao YM: methodology and formal analysis. Shi JW: conceptualization and writing—original draft preparation. Zhao Y: conceptualization, resources, and formal analysis. Zhang MH: methodology and project administration.

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Correspondence to Xilong Wu.

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Highlights

1. The runoff process is influenced by many factors, which makes accurate prediction of it particularly difficult. Therefore, we have chosen to investigate the changes in the runoff volume of the Xiangjiang River from 1980 to 2019.

2. The HP filter can decompose the original data into trend and cycle components, innovatively coupling the HP filter with a GM(1,1) model. The coupled model does not merely capture the trend signal of the runoff time series but also the seasonal signal.

3. The HP-GM(1,1) model has better prediction accuracy than the traditional GM(1,1) model, DGGM(1,1) model and SGM(1,1) model. It has the potential to be extended to other regions, expanding new ideas for nonlinear runoff prediction.

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Zhang, X., Wu, X., Xiao, Y. et al. Application of improved seasonal GM(1,1) model based on HP filter for runoff prediction in Xiangjiang River. Environ Sci Pollut Res 29, 52806–52817 (2022). https://doi.org/10.1007/s11356-022-19572-6

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  • DOI: https://doi.org/10.1007/s11356-022-19572-6

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