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DOI10.1002/2017WR020482
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
ISSN0043-1397
EISSN1944-7973
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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