Forecasting daily streamflow using online sequential extreme learning machines

Journal of Hydrology

Volumn 537, Issue , 2016, Pages 431-443

  Lima, Aranildo R ^a   Cannon, Alex J ^b   Hsieh, William W ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b ENVIRONMENT CANADA   (Canada)

Author keywords

Extreme learning machine (ELM); Forecast; Machine learning; Online sequential ELM (OSELM); Streamflow

Indexed keywords

ARTIFICIAL INTELLIGENCE; E-LEARNING; FEEDFORWARD NEURAL NETWORKS; FORECASTING; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; LEARNING SYSTEMS; LINEAR REGRESSION; NETWORK LAYERS; RADIAL BASIS FUNCTION NETWORKS; STREAM FLOW;

BRITISH COLUMBIA , CANADA; EXTREME LEARNING MACHINE; METEOROLOGICAL OBSERVATION; MULTIPLE LINEAR REGRESSIONS; ONLINE SEQUENTIAL ELM (OSELM); ONLINE SEQUENTIAL EXTREME LEARNING MACHINE; SEQUENTIAL LEARNING ALGORITHM; SINGLE-HIDDEN LAYER FEEDFORWARD NEURAL NETWORKS;

WEATHER FORECASTING;

ACCURACY ASSESSMENT; BENCHMARKING; DATA ASSIMILATION; HYDROMETEOROLOGY; MACHINE LEARNING; REGRESSION ANALYSIS; STREAMFLOW; WATERSHED; WEATHER FORECASTING;

BRITISH COLUMBIA; CANADA;

EID: 84962783538     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2016.03.017     Document Type: Article

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