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

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 LI^1,1、*, Yintang WANG^1,1, Qingfang HU^1,1, Yong LIU^1,1, Dingzhong LIU^2,2, and Tingting CUI^1,1

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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Fig. 1. 基于时变权重组合和贝叶斯修正的中长期径流预报方法流程Fig.1

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Fig. 2. 云南龙江水库位置示意图Fig.2

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Fig. 3. 龙江水库年入库径流与6—8月入库径流的RF与SVM模型的时变权重Fig.3

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Fig. 4. 本文方法与RF、SVM模型对龙江水库年、月入库径流预报精度的比较Fig.4

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Fig. 5. 本文方法与RF、SVM模型对龙江水库年径流及6—8月径流的预报结果Fig.5

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Fig. 6. 基于本文方法预报年径流的水文年型判断结果注：丰水年指超过累积频率(P)<37.5%,平水年指37.5%≤P<62.5%,枯水年指P≥62.5%。Fig.6

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Fig. 7. 基于本文方法预报的龙江水库1961—2017年逐月径流Fig.7

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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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