Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme
文献类型：期刊论文
| 作者 | Yang, Tiantian1; Gao, Xiaogang1; Sorooshian, Soroosh1; Li, Xin2 |
| 刊名 | WATER RESOURCES RESEARCH
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:000374706300005 |
| 出版者 | AMER GEOPHYSICAL UNION |
| URI标识 | http://www.irgrid.ac.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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来源：寒区旱区环境与工程研究所
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