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:1.8098
- @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