Daily reservoir runoff forecasting method using artificial neural network based on quantum-behaved particle swarm optimization

Water (Switzerland)

Volumn 7, Issue 8, 2015, Pages 4232-4246

  Cheng, Chun Tian ^a   Niu, Wen Jing ^a   Feng, Zhong Kai ^a   Shen, Jian Jian ^a   Chau, Kwok Wing ^b  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Artificial neural network; Daily runoff; Hybrid forecast; Quantum behaved particle swarm optimization (QPSO); Reservoir forecasting

Indexed keywords

ELECTRIC POWER SYSTEM CONTROL; FAULT TOLERANCE; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO); RAIN; RUNOFF;

ARTIFICIAL NEURAL NETWORK MODELING; CONVENTIONAL METHODS; GENERALIZATION PERFORMANCE; HIGH FAULT TOLERANCES; PARAMETERS SELECTION; QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION; RAINFALL-RUNOFF MODELS; STATISTICAL TECHNIQUES;

FORECASTING;

ARTIFICIAL NEURAL NETWORK; FORECASTING METHOD; LEARNING; OPTIMIZATION; PARAMETERIZATION; PERFORMANCE ASSESSMENT; RAINFALL-RUNOFF MODELING; RESERVOIR; TRAINING;

CHINA; GUIZHOU;

EID: 84940398455     PISSN: None     EISSN: 20734441     Source Type: Journal    
DOI: 10.3390/w7084232     Document Type: Article

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