Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines

Journal of Hydrology

Volumn 529, Issue , 2015, Pages 1617-1632

  Taormina, Riccardo ^a   Chau, Kwok Wing ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Extreme Learning Machines; Input variable selection; Multi objective optimization; Particle swarm optimization; Rainfall runoff; Streamflow prediction

Indexed keywords

ALGORITHMS; CATCHMENTS; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; PARTICLE SWARM OPTIMIZATION (PSO); RAIN; RUNOFF; SPECIFICATIONS; STREAM FLOW;

AKAIKE INFORMATION CRITERION; EVALUATION FRAMEWORK; EXTREME LEARNING MACHINE; INPUT VARIABLE SELECTION; RAINFALL RUNOFF; RAINFALL-RUNOFF MODELING; STREAMFLOW PREDICTION; SYNTHETIC RAINFALLS;

MULTIOBJECTIVE OPTIMIZATION;

ACCURACY ASSESSMENT; ALGORITHM; BASIN ANALYSIS; CATCHMENT; DATA ACQUISITION; DATA SET; MACHINE LEARNING; OPTIMIZATION; RAINFALL-RUNOFF MODELING; STREAMFLOW;

KENTUCKY; KENTUCKY RIVER; UNITED STATES;

EID: 84945467880     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2015.08.022     Document Type: Article

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