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

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@Article{Samadianfard:2013:Geofizika,

• 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 regression",
• 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 = "http://geofizika-journal.gfz.hr/vol30.htm",
• URL = "http://geofizika-journal.gfz.hr/vol_30/No1/30-1_Samadianfard_et_al.pdf",
• 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 concentrations.",
• notes = "http://geofizika-journal.gfz.hr/ UDC 551.509.313.4",

}

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

Citations