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

  • Authors:

  • J. C. M. Pires;M. C. M. Alvim-Ferraz;M. C. Pereira;F. G. Martins

  • Affiliations:

  • LEPAE, Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal;LEPAE, Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal;LEPAE, Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal;LEPAE, Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

This study aims to predict the next day hourly average tropospheric ozone (O"3) concentrations using genetic programming (GP). Due to the complexity of this problem, GP is an adequate methodology as it can optimize, 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 (NO"2) and O"3, and some meteorological variables (temperature -T; solar radiation - SR; relative humidity - RH; and wind speed - WS) measured 24h 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 O"3 concentrations. The original variables, T, RH and O"3 measured 24h before were considered significant inputs for prediction. The selected PC had also important contributions of the same variables and of NO"2. 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.