Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information
文献类型：期刊论文
| 作者 | Yang, Tiantian1,2; Asanjan, Ata Akbari1; Welles, Edwin2; Gao, Xiaogang1; Sorooshian, Soroosh1; Liu, Xiaomang3
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| 刊名 | WATER RESOURCES RESEARCH
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| 出版日期 | 2017-04-01 |
| 卷号 | 53期号:4页码:2786-2812 |
| ISSN号 | 0043-1397 |
| DOI | 10.1002/2017WR020482 |
| 通讯作者 | Yang, Tiantian(tiantiay@uci.edu) |
| 英文摘要 | Reservoirs are fundamental human-built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast-informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month-ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies. |
| WOS关键词 | NINO-SOUTHERN-OSCILLATION ; SUPPORT VECTOR REGRESSION ; WESTERN UNITED-STATES ; NEURAL-NETWORKS ; WATER-RESOURCES ; ATMOSPHERIC RIVERS ; VARIABLE SELECTION ; DECISION-MAKING ; RANDOM FORESTS ; RELEASE RULES |
| 资助项目 | DOE (Prime Award)[DE-IA0000018] ; CEC[300-15-005] ; CDWR Seasonal Forecasting via Database Enhancement Program (DWR)[4600010378] ; NSF CyberSEES project[CCF-1331915] ; NOAA/NESDIS/NCDC (Prime award)[NA09NES4400006] ; NOAA/NESDIS/NCDC (NCSU CICS) ; NOAA/NESDIS/NCDC[2009-1380-01] ; Army Research Office[W911NF-11-1-0422] |
| WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000403682600017 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 资助机构 | DOE (Prime Award) ; CEC ; CDWR Seasonal Forecasting via Database Enhancement Program (DWR) ; NSF CyberSEES project ; NOAA/NESDIS/NCDC (Prime award) ; NOAA/NESDIS/NCDC (NCSU CICS) ; NOAA/NESDIS/NCDC ; Army Research Office |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/63502]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yang, Tiantian |
| 作者单位 | 1.Univ Calif Irvine, Dept Civil & Environm Engn, Ctr Hydrometeorol & Remote Sensing, Irvine, CA 92697 USA 2.Deltares USA Inc, Silver Spring, MD 20910 USA 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Tiantian,Asanjan, Ata Akbari,Welles, Edwin,et al. Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information[J]. WATER RESOURCES RESEARCH,2017,53(4):2786-2812. |
| APA | Yang, Tiantian,Asanjan, Ata Akbari,Welles, Edwin,Gao, Xiaogang,Sorooshian, Soroosh,&Liu, Xiaomang.(2017).Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information.WATER RESOURCES RESEARCH,53(4),2786-2812. |
| MLA | Yang, Tiantian,et al."Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information".WATER RESOURCES RESEARCH 53.4(2017):2786-2812. |
入库方式： OAI收割
来源：地理科学与资源研究所
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