Improving artificial neural network model predictions of daily average PM10 concentrations by applying principle component analysis and implementing seasonal models

Journal of the Air and Waste Management Association

Volumn 65, Issue 7, 2015, Pages 800-809

  Taşpınar, Fatih ^a  

^a DUZCE UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; FORECASTING; NEURAL NETWORKS;

AERODYNAMIC DIAMETERS; ARTIFICIAL NEURAL NETWORK MODELING; ERROR BACK PROPAGATION; METEOROLOGICAL PARAMETERS; PARTICULATE MATTER; PM10 CONCENTRATION; PREDICTION PERFORMANCE; PRINCIPLE COMPONENT ANALYSIS;

PRINCIPAL COMPONENT ANALYSIS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; HEATING; METEOROLOGICAL PHENOMENA; METEOROLOGY; PARTICULATE MATTER; PERCEPTRON; PREDICTION; PRIORITY JOURNAL; SEASON; SUMMER; WINTER; AIR POLLUTANT; ANALYSIS; PARTICLE SIZE; PRINCIPAL COMPONENT ANALYSIS; THEORETICAL MODEL;

AIR POLLUTANT; PARTICULATE MATTER;

AIR POLLUTANTS; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); PARTICLE SIZE; PARTICULATE MATTER; PRINCIPAL COMPONENT ANALYSIS; SEASONS;

EID: 84944029643     PISSN: 10962247     EISSN: 21622906     Source Type: Journal    
DOI: 10.1080/10962247.2015.1019652     Document Type: Article

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