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Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series

Author

Listed:
  • Sungwon Kim

    (Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, Korea)

  • Meysam Alizamir

    (Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan 65181-15743, Iran)

  • Nam Won Kim

    (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea)

  • Ozgur Kisi

    (Department of Civil Engineering, School of Technology, Ilia State University, Tbilisi 0162, Georgia
    Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam)

Abstract

Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.

Suggested Citation

  • Sungwon Kim & Meysam Alizamir & Nam Won Kim & Ozgur Kisi, 2020. "Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9720-:d:448823
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    References listed on IDEAS

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    Cited by:

    1. Junhao Wu & Zhaocai Wang & Yuan Hu & Sen Tao & Jinghan Dong, 2023. "Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 937-953, January.
    2. Srishti Gaur & Arnab Bandyopadhyay & Rajendra Singh, 2021. "From Changing Environment to Changing Extremes: Exploring the Future Streamflow and Associated Uncertainties Through Integrated Modelling System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1889-1911, April.

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    More about this item

    Keywords

    streamflow forecasting; Bayesian model averaging; multivariate adaptive regression spline; M5 model tree; Kernel extreme learning machines; South Korea;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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