Analysis of surface ozone using a recurrent neural network

Science of the Total Environment

Volumn 514, Issue , 2015, Pages 379-387

  Biancofiore, Fabio ^a   Verdecchia, Marco ^a   Di Carlo, Piero ^a   Tomassetti, Barbara ^a   Aruffo, Eleonora ^a   Busilacchio, Marcella ^a   Bianco, Sebastiano ^b   Di Tommaso, Sinibaldo ^b   Colangeli, Carlo ^b  

^a UNIVERSITY OF L'AQUILA   (Italy)

^b ARTA Agenzia Regionale per l'Ambiente   (Italy)

Author keywords

Boundary layer; Model comparison; Neural network; Ozone; Troposphere; Urban pollution

Indexed keywords

BOUNDARY LAYERS; FORECASTING; LINEAR REGRESSION; MEAN SQUARE ERROR; METEOROLOGY; MULTILAYER NEURAL NETWORKS; NETWORK ARCHITECTURE; NEURAL NETWORKS; NITROGEN OXIDES; OZONE; OZONE LAYER; POLLUTION; TROPOSPHERE;

CORRELATION COEFFICIENT; FEED-FORWARD NEURAL ARCHITECTURES; METEOROLOGICAL PARAMETERS; MODEL COMPARISON; MULTIPLE LINEAR REGRESSION MODELS; NORMALIZED MEAN SQUARED ERRORS; PHOTOCHEMICAL PARAMETERS; URBAN POLLUTIONS;

RECURRENT NEURAL NETWORKS;

NITROGEN DIOXIDE; OZONE;

AIR QUALITY; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC POLLUTION; BOUNDARY LAYER; CONCENTRATION (COMPOSITION); NITROGEN DIOXIDE; OZONE; TROPOSPHERE; URBAN ATMOSPHERE; URBAN POLLUTION;

AIR ANALYSIS; AIR MONITORING; AIR POLLUTION; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; FORECASTING; ITALY; METEOROLOGY; MULTIPLE LINEAR REGRESSION ANALYSIS; PHOTOCHEMISTRY; SEASONAL VARIATION; TREND STUDY; WEATHER;

ITALY;

EID: 84922566952     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2015.01.106     Document Type: Article

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