Prediction of tropospheric ozone concentrations: Application of a methodology based on the Darwin's Theory of Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "J. C. M. Pires and M. C. M. Alvim-Ferraz and 
                 M. C. Pereira and F. G. Martins",
  title =        "Prediction of tropospheric ozone concentrations:
                 Application of a methodology based on the Darwin's
                 Theory of Evolution",
  journal =      "Expert Systems with Applications",
  volume =       "38",
  number =       "3",
  pages =        "1903--1908",
  year =         "2011",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2010.07.122",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Tropospheric
                 ozone, Air pollution modelling",
  abstract =     "This study aims to predict the next day hourly average
                 tropospheric ozone (O3) concentrations using genetic
                 programming (GP). Due to the complexity of this
                 problem, GP is an adequate methodology as it can
                 optimise, simultaneously, the structure of the model
                 and its parameters. It is an artificial intelligence
                 methodology that uses the same principles of the
                 Darwinian Theory of Evolution. GP enables the automatic
                 generation of mathematical expressions that are
                 modified following an iterative process applying
                 genetic operations.

                 The inputs of the models were the hourly average
                 concentrations of carbon monoxide (CO), nitrogen oxide
                 (NO), nitrogen dioxide (NO2) and O3, and some
                 meteorological variables (temperature - T; solar
                 radiation - SR; relative humidity - RH; and wind speed
                 - WS) measured 24 h before. GP was also applied to the
                 principal components (PC) obtained from these
                 variables. The analysed period was from May to July
                 2004 divided in training and test periods.

                 GP was able to select the most relevant variables for
                 prediction of O3 concentrations. The original
                 variables, T, RH and O3 measured 24 h before were
                 considered significant inputs for prediction. The
                 selected PC had also important contributions of the
                 same variables and of NO2. GP models using the original
                 variables presented better performance in training
                 period and worse performance in test period when
                 compared with the models obtained using PC. The results
                 achieved using the GP methodology demonstrated that it
                 can be very useful to solve several environmental
                 complex problems.",

Genetic Programming entries for Jose Carlos Magalhaes Pires Maria da Conceicao Machado Alvim Ferraz Maria do Carmo da Silva Pereira Fernando Gomes Martins