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:1.3973

@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, air
                 pollution, O3, surface ozone, multigene symbolic
                 regression, multilayer perceptron neural network,
                 prediction",
  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",
  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.",
}

Genetic Programming entries for Alaa Sheta Hossam Faris

Citations