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	Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme
	Yang, Tiantian1; Gao, Xiaogang1; Sorooshian, Soroosh1; Li, Xin2
刊名	WATER RESOURCES RESEARCH
	2016-03-01
卷号	52期号:3页码:1626-1651
关键词	reservoir operation decision tree controlled outflow California
ISSN号	0043-1397
DOI	10.1002/2015WR017394
通讯作者	Yang, Tiantian(tiantiay@uci.edu)
英文摘要	The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others.
收录类别	SCI
WOS关键词	SYSTEM SIMULATION ; NEURAL-NETWORK ; RANDOM FORESTS ; FUZZY-LOGIC ; RULES ; MODEL ; PREDICTION ; SELECTION ; INFORMATION ; COMPLEX
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
WOS类目	Environmental Sciences ; Limnology ; Water Resources
语种	英语
出版者	AMER GEOPHYSICAL UNION
WOS记录号	WOS:000374706300005
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2557311
专题	寒区旱区环境与工程研究所
通讯作者	Yang, Tiantian
作者单位	1.Univ Calif Irvine, Dept Civil & Environm Engn, Irvine, CA USA 2.Chinese Acad Sci, Arid Reg Environm & Engn Res Inst, Lanzhou, Peoples R China
推荐引用方式 GB/T 7714	Yang, Tiantian,Gao, Xiaogang,Sorooshian, Soroosh,et al. Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme[J]. WATER RESOURCES RESEARCH,2016,52(3):1626-1651.
APA	Yang, Tiantian,Gao, Xiaogang,Sorooshian, Soroosh,&Li, Xin.(2016).Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme.WATER RESOURCES RESEARCH,52(3),1626-1651.
MLA	Yang, Tiantian,et al."Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme".WATER RESOURCES RESEARCH 52.3(2016):1626-1651.

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