Correction methods for statistical models in tropospheric ozone forecasting

Atmospheric Environment

Volumn 45, Issue 14, 2011, Pages 2413-2417

  Pires, J C M ^a   Martins, F G ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

Air pollution modelling; Artificial neural network; Correction methods; Multiple linear regression; Tropospheric ozone

Indexed keywords

AIR POLLUTION MODELLING; ARTIFICIAL NEURAL NETWORK; CORRECTION METHODS; MULTIPLE LINEAR REGRESSION; TROPOSPHERIC OZONE;

AIR QUALITY; ATMOSPHERIC HUMIDITY; CARBON MONOXIDE; CONCENTRATION (PROCESS); LINEAR REGRESSION; NITRIC OXIDE; NITROGEN OXIDES; OZONE; POLLUTION; SULFUR; SULFUR DIOXIDE; TROPOSPHERE; WEATHER FORECASTING;

NEURAL NETWORKS;

CARBON MONOXIDE; NITROGEN DIOXIDE; NITROGEN OXIDE; OZONE; SULFUR DIOXIDE;

ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC MODELING; ATMOSPHERIC POLLUTION; CLIMATE PREDICTION; CONCENTRATION (COMPOSITION); ERROR ANALYSIS; ERROR CORRECTION; NUMERICAL MODEL; OZONE; PERFORMANCE ASSESSMENT; REGRESSION ANALYSIS; TROPOSPHERE; WEATHER FORECASTING; WIND VELOCITY;

ARTICLE; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC DEPOSITION; CONTROLLED STUDY; ERROR; FORECASTING; HUMIDITY; MULTIPLE LINEAR REGRESSION ANALYSIS; PRIORITY JOURNAL; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE; TEMPERATURE; TROPOSPHERE; WIND;

EID: 79953269771     PISSN: 13522310     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2011.02.011     Document Type: Article

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