Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

Atmospheric Environment

Volumn 184, Issue , 2018, Pages 129-139

  Gao, Meng ^a   Yin, Liting ^a,b   Ning, Jicai ^a  

^a YANTAI INSTITUTE OF COASTAL ZONE RESEARCH   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Air pollution; Artificial neural network; Monte Carlo simulation; Sensitivity analysis; Uncertainty analysis

Indexed keywords

AIR POLLUTION; AIR QUALITY; ATMOSPHERIC PRESSURE; FORECASTING; MONTE CARLO METHODS; OZONE; SENSITIVITY ANALYSIS; TOPOGRAPHY; UNCERTAINTY ANALYSIS; WIND;

AIR POLLUTANT CONCENTRATIONS; AMBIENT OZONE CONCENTRATION; ARCHITECTURE OF NETWORKS; ARTIFICIAL NEURAL NETWORK MODELING; METEOROLOGICAL PARAMETERS; MONTE CARLO ANALYSIS; PHOTOCHEMICAL PARAMETERS; UNCERTAINTY AND SENSITIVITY ANALYSIS;

NEURAL NETWORKS;

OZONE;

AIR TEMPERATURE; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC MODELING; ATMOSPHERIC POLLUTION; ATMOSPHERIC PRESSURE; CONCENTRATION (COMPOSITION); ESTIMATION METHOD; METROPOLITAN AREA; MONTE CARLO ANALYSIS; OZONE; SOLAR RADIATION; URBAN POLLUTION; WIND VELOCITY;

AIR POLLUTION; AIR QUALITY; AMBIENT AIR; ARTICLE; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC PRESSURE; CHINA; CONCENTRATION (PARAMETERS); METEOROLOGY; MONTE CARLO METHOD; PHOTOCHEMISTRY; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; UNCERTAINTY; URBAN AREA; WARMING; WIND;

CHINA;

EID: 85046134663     PISSN: 13522310     EISSN: 18732844     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2018.03.027     Document Type: Article

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