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A Runoff Prediction Model Based on Nonhomogeneous Markov Chain

Author

Listed:
  • Wei Li

    (Hebei University of Engineering)

  • Xiaosheng Wang

    (Hebei University of Engineering)

  • Shujiang Pang

    (Hebei University of Engineering)

  • Haiying Guo

    (Hebei University of Engineering)

Abstract

Runoff prediction is one of the important research fields of hydrology. As for the runoff series with unstable, poor periodicity and non-obvious tendency, this paper solves the problem that the general traditional models are not suitable for the short and medium-term prediction of such runoff series. To describe the nonhomogeneous characteristics of runoff series, a novel prediction model is established based on a nonhomogeneous Markov chain (NHMC-RPM). In this model, the probability distribution function of weekly runoff is obtained and the predicted value is calculated using the expected state. Taking the Yellow River as a case, the prediction results show that the NHMC-RPM is more accurate than other traditional models. The model presented in this work may be used to deal with similar runoff or other series data, as well as provide a practical approach for river managers to predict short and medium-term runoff.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:4:d:10.1007_s11269-022-03091-7
    DOI: 10.1007/s11269-022-03091-7
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Munkhammar, Joakim & van der Meer, Dennis & Widén, Joakim, 2021. "Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model," Applied Energy, Elsevier, vol. 282(PA).
    5. Huaping Huang & Zhongmin Liang & Binquan Li & Dong Wang & Yiming Hu & Yujie Li, 2019. "Combination of Multiple Data-Driven Models for Long-Term Monthly Runoff Predictions Based on Bayesian Model Averaging," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3321-3338, July.
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