Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model

doi:10.3390/w9010009

	Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model
	Liu, Yong 1,2; Sang, Yan-Fang 1,2; Li, Xinxin 1,3; Hu, Jian 1; Liang, Kang 1
刊名	Water
	2017
卷号	9 期号:1 页码:11
关键词	long-term streamflow forecasting relevance vector machine support vector machine hydrological process
ISSN号	2073-4441
DOI	10.3390/w9010009
通讯作者	Sang, Yan-Fang(sangyf@igsnrr.ac.cn)
英文摘要	Long-term streamflow forecasting is crucial to reservoir scheduling and water resources management. However, due to the complexity of internally physical mechanisms in streamflow process and the influence of many random factors, long-term streamflow forecasting is a difficult issue. In the article, we mainly investigated the ability of the Relevance Vector Machine (RVM) model and its applicability for long-term streamflow forecasting. We chose the Dahuofang (DHF) Reservoir in Northern China and the Danjiangkou (DJK) Reservoir in Central China as the study sites, and selected the 500 hpa geopotential height in the northern hemisphere and the sea surface temperatures in the North Pacific as the predictor factors of the RVM model and the Support Vector Machine (SVM) model, and then conducted annual streamflow forecasting. Results indicate that forecasting results in the DHF Reservoir is much better than that in the DJK Reservoir when using SVM, because streamflow process in the latter basin has a magnitude bigger than 1000 m(3)/s. Comparatively, accurate forecasting results in both the two basins can be gotten using the RVM model, with the Nash Sutcliffe efficiency coefficient bigger than 0.7, and they are much better than those gotten from the SVM model. As a result, the RVM model can be an effective approach for long-term streamflow forecasting, and it also has a wide applicability for the streamflow process with a discharge magnitude from dozen to thousand cubic meter per second.
资助项目	National Natural Science Foundation of China[91647110] ; Bingwei Excellent Talents from the Institute of Geographic Sciences and Natural Resources Research, CAS ; Youth Innovation Promotion Association CAS ; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences[WL2015003] ; Water Resources Public-welfare Project of China[Y515010] ; Water Resources Public-welfare Project of China[Y516012]
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; PREDICTION ; RIVER ; OSCILLATION ; TRENDS ; ERROR ; FLOW
WOS研究方向	Water Resources
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000392939900009
资助机构	National Natural Science Foundation of China ; Bingwei Excellent Talents from the Institute of Geographic Sciences and Natural Resources Research, CAS ; Youth Innovation Promotion Association CAS ; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences ; Water Resources Public-welfare Project of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/65038]
专题	中国科学院地理科学与资源研究所
通讯作者	Sang, Yan-Fang
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 2.Nanjing Hydraul Res Inst, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210029, Jiangsu, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yong,Sang, Yan-Fang,Li, Xinxin,et al. Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model[J]. Water,2017,9(1):11.
APA	Liu, Yong,Sang, Yan-Fang,Li, Xinxin,Hu, Jian,&Liang, Kang.(2017).Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model.Water,9(1),11.
MLA	Liu, Yong,et al."Long-Term Streamflow Forecasting Based on Relevance Vector Machine Model".Water 9.1(2017):11.