Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

Water Resources Research

Volumn 53, Issue 4, 2017, Pages 2786-2812

  Yang, Tiantian ^a,b   Asanjan, Ata Akbari ^a   Welles, Edwin ^b   Gao, Xiaogang ^a   Sorooshian, Soroosh ^a   Liu, Xiaomang ^c  

^a University of California at Irvine   (United States)

^b Deltares USA Inc   (United States)

^c INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

Author keywords

artificial intelligence; climate indices; Danjiangkou Reservoir; data mining; reservoir inflow; Trinity Lake

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLIMATE MODELS; CLIMATOLOGY; DATA MINING; DECISION TREES; DEEP NEURAL NETWORKS; FORECASTING; NEURAL NETWORKS; STREAM FLOW; SURFACE WATERS;

CLIMATE INDEX; CLIMATIC INFORMATIONS; DANJIANGKOU RESERVOIR; HYDROLOGICAL CONDITION; RANDOM FOREST ALGORITHM; RESERVOIR INFLOW; STATISTICAL PERFORMANCE; SUPPORT VECTOR REGRESSION (SVR);

RESERVOIRS (WATER);

ALGORITHM; ARTIFICIAL INTELLIGENCE; CLIMATE CLASSIFICATION; CLIMATE CONDITIONS; DATA MINING; FORECASTING METHOD; GROUNDWATER-SURFACE WATER INTERACTION; HEADWATER; INFLOW; INFORMATION SYSTEM; RESERVOIR; STREAMFLOW;

CALIFORNIA; CHINA; DANJIANGKOU RESERVOIR; TRINITY RIVER [CALIFORNIA]; UNITED STATES;

EID: 85017285525     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2017WR020482     Document Type: Article

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