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Combination of Multiple Data-Driven Models for Long-Term Monthly Runoff Predictions Based on Bayesian Model Averaging

Author

Listed:
  • Huaping Huang

    (Hohai University
    University of Melbourne)

  • Zhongmin Liang

    (Hohai University)

  • Binquan Li

    (Hohai University)

  • Dong Wang

    (Changjiang Water Resources Commission)

  • Yiming Hu

    (Hohai University)

  • Yujie Li

    (Hohai University)

Abstract

Accurate and reliable long-term runoff forecasting is very important for water resource system planning and management. This study utilized three data-driven models to simulate and forecast the monthly runoff series of the Huangzhuang hydrological station from 1981 to 2017. To improve the accuracy and reduce the uncertainty, two model averaging techniques were applied to merge forecast results of the different models, and 90% confidence intervals were derived using Monte Carlo sampling. Several indices were used to evaluate the results of three data-driven models and two model averaging techniques. Among the many discoveries in this paper, the following stand out: (i) in general, the random forest (RF) algorithm presented nearly the same accuracy as did the artificial neural network (ANN) algorithm, and both were superior to the support vector machine (SVM) method; however, none of the models consistently provided the best result in all months; (ii) the comparison of the deterministic results indicated that Copula-Bayesian model averaging (BMA) exhibited smaller errors than did BMA, especially for the points whose uniform quantiles ranged within (0.125, 0.35) and (0.5, 0.625); and (iii) in most cases, the 90% confidence interval of the Copula-BMA scheme had higher containing ratio values, smaller average relative bandwidth values in the high-flow months, and smaller average relative deviation amplitudes than did BMA.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:9:d:10.1007_s11269-019-02305-9
    DOI: 10.1007/s11269-019-02305-9
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    References listed on IDEAS

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    1. Jianhua Xu & Yaning Chen & Weihong Li & Qin Nie & Chunan Song & Chunmeng Wei, 2014. "Integrating Wavelet Analysis and BPANN to Simulate the Annual Runoff With Regional Climate Change: A Case Study of Yarkand River, Northwest China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2523-2537, July.
    2. Mustafa Turan & Mehmet Yurdusev, 2014. "Predicting Monthly River Flows by Genetic Fuzzy Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4685-4697, October.
    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. Alpaslan Yarar, 2014. "A Hybrid Wavelet and Neuro-Fuzzy Model for Forecasting the Monthly Streamflow Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 553-565, January.
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