Predicting PM2.5 concentrations at a regional background station using second order self-organizing fuzzy neural network

Atmosphere

Volumn 8, Issue 1, 2017, Pages

  Qiao, Junfei ^a   Cai, Jie ^a   Han, Honggui ^a   Cai, Jianxian ^a  

^a BEIJING UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Dominating factors; PM2.5; Predicting; Principal component analysis; SOG SASOFNN

Indexed keywords

ATMOSPHERIC AEROSOLS; FORECASTING; FUZZY INFERENCE; FUZZY LOGIC; FUZZY NEURAL NETWORKS; SENSITIVITY ANALYSIS; WIND;

AEROSOL OPTICAL DEPTHS; COEFFICIENT OF DETERMINATION; DOMINATING FACTORS; FINE PARTICULATE MATTER; METEOROLOGICAL PARAMETERS; PREDICTING; SELF-ORGANIZING FUZZY NEURAL NETWORK; SOG-SASOFNN;

PRINCIPAL COMPONENT ANALYSIS;

AEROSOL; ALGORITHM; ARTIFICIAL NEURAL NETWORK; FUZZY MATHEMATICS; OPTICAL DEPTH; PARTICULATE MATTER; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; RELATIVE HUMIDITY; WIND DIRECTION; WIND VELOCITY;

BEIJING [CHINA]; CHINA; SHANGDIANZI;

EID: 85011060295     PISSN: None     EISSN: 20734433     Source Type: Journal    
DOI: 10.3390/atmos8010010     Document Type: Article

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