fevereiro 2015 vol. 1 num. 2 - XX Congresso Brasileiro de Engenharia Química

Artigo - Open Access.

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PREDICTION OF TROPOSPHERIC OZONE AT RIO DE JANEIRO CITY FROM PRIMARY POLLUENTS AND METEOROLOGICAL FACTORS USING ARTIFICIAL NEURAL NETWORKS (ANN) AND SUPPORT VECTOR MACHINE (SVM) REGRESSION

OLIVEIRA, G. C. G. de; LUNA, A. S.; PAREDES, M. L. L.; CORRÊA, S. M.;

Artigo:

Tropospheric ozone is well known as an extremely important factor due to its strong environmental impact. The air quality depends on emissions, meteorology and topography. NO2, NO, NOx, CO, O3, scalar wind speed, solar radiation, temperature, and relative humidity (HUM) were monitored. These data sets were collected by the mobile station monitoring located at PUC-Rio and UERJ between 2011 and 2012, from the Secretary of the Environment of Rio de Janeiro. This study aimed at the prediction of O3 from primary pollutants and meteorological factors. The obtained results of ANN and SVM regression techniques were acceptable to the dataset UERJ presenting coefficient of determination (R

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DOI: 10.5151/chemeng-cobeq2014-0227-26447-181079

Referências bibliográficas
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Como citar:

OLIVEIRA, G. C. G. de; LUNA, A. S.; PAREDES, M. L. L.; CORRÊA, S. M.; "PREDICTION OF TROPOSPHERIC OZONE AT RIO DE JANEIRO CITY FROM PRIMARY POLLUENTS AND METEOROLOGICAL FACTORS USING ARTIFICIAL NEURAL NETWORKS (ANN) AND SUPPORT VECTOR MACHINE (SVM) REGRESSION", p. 6784-6792 . In: Anais do XX Congresso Brasileiro de Engenharia Química - COBEQ 2014 [= Blucher Chemical Engineering Proceedings, v.1, n.2]. São Paulo: Blucher, 2015.
ISSN 2359-1757, DOI 10.5151/chemeng-cobeq2014-0227-26447-181079

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