Comparative analysis of ozone level prediction models using gene expression programming and multiple linear regression

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  author =       "Saeed Samadianfard and Reza Delirhasannia and 
                 Ozgur Kisi and Elena Agirre-Basurko",
  title =        "Comparative analysis of ozone level prediction models
                 using gene expression programming and multiple linear
  journal =      "Geofizika",
  year =         "2013",
  volume =       "30",
  number =       "1",
  pages =        "43--74",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, air quality modelling, multiple
                 linear regression, ozone level forecasting, Bilbao
                 area, Spain",
  ISSN =         "0352-3659",
  publisher =    "Geophysical Institute in Zagreb",
  URL =          "",
  URL =          "",
  size =         "32 pages",
  abstract =     "Ground-level ozone (O3) has been a serious air
                 pollution problem for several decades and in many
                 metropolitan areas, due to its adverse impact on the
                 human respiratory system. Therefore, to reduce the
                 risks of O3 related damages, developing, maintaining
                 and improving short term ozone forecasting models is
                 needed. This paper presents the results of two
                 prognostic models including gene expression programming
                 (GEP), which is a variant of genetic programming (GP),
                 and multiple linear regression (MLR) to forecast ozone
                 levels in real-time up to 6 hours ahead at four
                 stations in Bilbao, Spain. The inputs to the GEP were
                 meteorological conditions (wind speed and direction,
                 temperature, relative humidity, pressure, solar
                 radiation and thermal gradient), hourly ozone levels
                 and traffic parameters (number of vehicles, occupation
                 percentage and velocity), which were measured in the
                 years of 1993-94. The performances of developed models
                 were compared with observed values and were evaluated
                 using specific performance measurements for the air
                 quality models established in the Model Validation Kit
                 and recommended by the US Environmental Protection
                 Agency. It was found that the GEP in most cases gives
                 superior predictions. Finally it can be concluded on
                 the basis of the results of this study that gene
                 expression programming appears to be a promising
                 technique for the prediction of pollutant
  notes =        " UDC 551.509.313.4",

Genetic Programming entries for Saeed Samadianfard Reza Delirhasannia Ozgur Kisi Elena Agirre-Basurko