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

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{Sheta:2015:IJACSA,

• title = "Influence of Nitrogen-di-Oxide, Temperature and Relative Humidity on Surface Ozone Modeling Process Using Multigene Symbolic Regression Genetic Programming",
• author = "Alaa F. Sheta and Hossam Faris",
• journal = "International Journal of Advanced Computer Science and Applications (IJACSA)",
• year = "2015",
• number = "6",
• volume = "6",
• keywords = "genetic algorithms, genetic programming, ANN, SVM, air pollution, O3, surface ozone, multigene symbolic regression, multilayer perceptron neural network, prediction, NO2, CO, SO2, NOx, O3",
• bibsource = "OAI-PMH server at thesai.org",
• description = "International Journal of Advanced Computer Science and Applications(IJACSA), 6(6), 2015",
• language = "eng",
• oai = "oai:thesai.org:10.14569/IJACSA.2015.060637",
• URL = "http://thesai.org/Downloads/Volume6No6/Paper_37-Influence_of_Nitrogen_di_Oxide_Temperature.pdf",
• URL = "http://dx.doi.org/10.14569/IJACSA.2015.060637",
• DOI = "doi:10.14569/IJACSA.2015.060637",
• publisher = "The Science and Information (SAI) Organization",
• 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. Modelling and observing changes in the Ozone layer have been studied in the past. Understanding the dynamics of the pollutants features that influence Ozone is explored 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 modelling Ozone is presented. The developed results show that GP outperform the ANN.",
• notes = "Also known as \cite{Sheta2015}",

}

Genetic Programming entries for Alaa Sheta Hossam Faris

Citations