IGSNRR OpenIR
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
Source PublicationWATER RESOURCES RESEARCH
ISSN0043-1397
Volume53Issue:4Pages:2786-2812
Corresponding AuthorYang, Tiantian(tiantiay@uci.edu)
AbstractReservoirs 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.
DOI10.1002/2017WR020482
WOS KeywordNINO-SOUTHERN-OSCILLATION ; SUPPORT VECTOR REGRESSION ; WESTERN UNITED-STATES ; NEURAL-NETWORKS ; WATER-RESOURCES ; ATMOSPHERIC RIVERS ; VARIABLE SELECTION ; DECISION-MAKING ; RANDOM FORESTS ; RELEASE RULES
Indexed BySCI
Language英语
Funding ProjectDOE (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]
Funding OrganizationDOE (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
WOS Research AreaEnvironmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
WOS SubjectEnvironmental Sciences ; Limnology ; Water Resources
WOS IDWOS:000403682600017
PublisherAMER GEOPHYSICAL UNION
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/63503
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYang, Tiantian
Affiliation1.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
Recommended Citation
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.
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