Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2017WR020482 |
Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information | |
Yang, Tiantian1,2; Asanjan, Ata Akbari1; Welles, Edwin2; Gao, Xiaogang1; Sorooshian, Soroosh1; Liu, Xiaomang3 | |
2017-04-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:4 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Peoples R China |
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. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000403682600017 |
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 |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/22042 |
专题 | 资源环境科学 |
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). |
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). |
MLA | Yang, Tiantian,et al."Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information".WATER RESOURCES RESEARCH 53.4(2017). |
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