NDLI: Influence of Nitrogen-di-Oxide, Temperature and Relative Humidity on Surface Ozone Modeling Process Using Multigene Symbolic Regression Genetic Programming

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Influence of Nitrogen-di-Oxide, Temperature and Relative Humidity on Surface Ozone Modeling Process Using Multigene Symbolic Regression Genetic Programming

Content Provider	Semantic Scholar
Author	Sheta, Alaa F. Faris, Hossam
Copyright Year	2015
Abstract	Automatic monitoring, data collection, analysis and prediction of environmental changes is essential for all living things. Understanding future climate changes does not only helps in measuring the influence on people life, habits, agricultural and health but also helps in avoiding disasters. Giving the high emission of chemicals on air, scientist discovered the growing depletion in ozone layer. This causes a serious environmental problem. Modeling and observing changes in the Ozone layer have been studied in the past. Understanding the dynamics of the pollutants features that influence Ozone is ex-plored in this article. A short term prediction model for surface Ozone is offered using Multigene Symbolic Regression Genetic Programming (GP). The proposed model customs Nitrogen-di-Oxide, Temperature and Relative Humidity as the main features to predict the Ozone level. Moreover, a comparison between GP and Artificial Neural Network (ANN) in modeling Ozone is presented. The developed results show that GP outperform the ANN.
File Format	PDF HTM / HTML
DOI	10.14569/IJACSA.2015.060637
Volume Number	6
Alternate Webpage(s)	http://thesai.org/Downloads/Volume6No6/Paper_37-Influence_of_Nitrogen_di_Oxide_Temperature.pdf
Alternate Webpage(s)	http://www.thesai.org/Downloads/Volume6No6/Paper_37-Influence_of_Nitrogen_di_Oxide_Temperature.pdf
Alternate Webpage(s)	https://doi.org/10.14569/IJACSA.2015.060637
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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