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https://doi.org/10.37358/Rev.Chim.1949

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Revista de Chimie (Rev. Chim.), Year 2017, Volume 68, Issue 10, 2224-2227

https://doi.org/10.37358/RC.17.10.5860

Camelia Gavrila

Ozone Concentration Prediction using Artificial Neural Networks

Abstract:

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.
Keywords:
air pollution; troposphere ozone; artificial neuronal networks; hyperbolic tangent model

Issue: 2017, Volume 68, Issue 10
Pages: 2224-2227
Publication date: 2017/11/15
https://doi.org/10.37358/RC.17.10.5860
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Creative Commons License
This article is published under the Creative Commons Attribution 4.0 International License
Citation Styles
Cite this article as:
GAVRILA, C., Ozone Concentration Prediction using Artificial Neural Networks, Rev. Chim., 68(10), 2017, 2224-2227.

Vancouver
Gavrila C. Ozone Concentration Prediction using Artificial Neural Networks. Rev. Chim.[internet]. 2017 Oct;68(10):2224-2227. Available from: https://doi.org/10.37358/RC.17.10.5860


APA 6th edition
Gavrila, C., (2017). Ozone Concentration Prediction using Artificial Neural Networks. Revista de Chimie, 68(10), 2224-2227. https://doi.org/10.37358/RC.17.10.5860


Harvard
Gavrila, C., (2017). 'Ozone Concentration Prediction using Artificial Neural Networks', Revista de Chimie, 68(10), pp. 2224-2227. https://doi.org/10.37358/RC.17.10.5860


IEEE
C. Gavrila, "Ozone Concentration Prediction using Artificial Neural Networks". Revista de Chimie, vol. 68, no. 10, pp. 2224-2227, 2017. [online]. https://doi.org/10.37358/RC.17.10.5860


Text
Camelia Gavrila,
Ozone Concentration Prediction using Artificial Neural Networks,
Revista de Chimie,
Volume 68, Issue 10,
2017,
Pages 2224-2227,
ISSN 2668-8212,
https://doi.org/10.37358/RC.17.10.5860.
(https://revistadechimie.ro/Articles.asp?ID=5860)
Keywords: air pollution; troposphere ozone; artificial neuronal networks; hyperbolic tangent model


RIS
TY - JOUR
T1 - Ozone Concentration Prediction using Artificial Neural Networks
A1 - Gavrila, Camelia
JF - Revista de Chimie
JO - Rev. Chim.
PB - Revista de Chimie SRL
SN - 2668-8212
Y1 - 2017
VL - 68
IS - 10
SP - 2224
EP - 2227
UR - https://doi.org/10.37358/RC.17.10.5860
KW - air pollution
KW - troposphere ozone
KW - artificial neuronal networks
KW - hyperbolic tangent model
ER -


BibTex
@article{RevCh2017P2224,
author = {Gavrila Camelia},
title = {Ozone Concentration Prediction using Artificial Neural Networks},
journal = {Revista de Chimie},
volume = {68},
number = {10},
pages = {2224-2227},
year = {2017},
issn = {2668-8212},
doi = {https://doi.org/10.37358/RC.17.10.5860},
url = {https://revistadechimie.ro/Articles.asp?ID=5860}
}
 
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