Progress in Geography, Volume. 39, Issue 4, 643(2020)

Mid- and long-term runoff prediction based on time-varying weight combination and Bayesian correction

Lingjie LI1,1、*, Yintang WANG1,1, Qingfang HU1,1, Yong LIU1,1, Dingzhong LIU2,2, and Tingting CUI1,1
Author Affiliations
  • 1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
  • 1南京水利科学研究院水文水资源与水利工程科学国家重点实验室,南京 210029
  • 2Yunnan Longjiang Water Conservancy Project Development Co., Ltd., Dehong 678400, Yunnan, China
  • 2云南龙江水利枢纽开发有限公司,云南 德宏 678400
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    References(9)

    [8] Chowdhury S, Sharma A. Multisite seasonal forecast of arid river flows using a dynamic model combination approach[J]. Water Resources Research, 45, 2381-2386(2009).

    [12] Wu J H, Qian H, Li P et al. A system-theory-based model for monthly river runoff forecasting: Model calibration and optimization[J]. Journal of Hydrology and Hydromechanics, 62, 82-88(2014).

    [13] Liang Z M, Li Y J, Hu Y M et al. A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework[J]. Theoretical and Applied Climatology, 133, 137-149(2018).

    [17] Krzysztofowicz R. Bayesian models of forecasted time series 1[J]. Journal of the American Water Resources Association, 21, 805-814(1985).

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    Lingjie LI, Yintang WANG, Qingfang HU, Yong LIU, Dingzhong LIU, Tingting CUI. Mid- and long-term runoff prediction based on time-varying weight combination and Bayesian correction[J]. Progress in Geography, 2020, 39(4): 643

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    Paper Information

    Received: Nov. 3, 2019

    Accepted: --

    Published Online: Oct. 16, 2020

    The Author Email:

    DOI:10.18306/dlkxjz.2020.04.011

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