Forecasting of ozone pollution using artificial neural networks

Reem S. Ettouney, Farouq S. Mjalli, John G. Zaki, Mahmoud A. El-Rifai, Hisham M. Ettouney

Research output: Contribution to journalArticle

12 Citations (Scopus)


Purpose: The objective of this study is to develop and validate a neural-based modelling methodology applicable to site-specific short- and medium-term ozone concentration forecasting. A novel modelling technique utilizing two feed forward artificial neural networks (FFNN) is developed to improve the performance of time series predictions. Design/methodology/approach: Air pollution and meteorological data were collected for one year in two locations in Kuwait. The hourly averages of the data were processed to generate a covariance matrix and analyzed to generate the principal component method. A two-FFNN model is then used to predict the actual data. Findings: The newly developed model improves the prediction accuracy over the conventional method. Owing to the presence of noise and other minor disturbances in the data, shorter-range modelling gives better modelling results. Originality/value: A novel modelling technique is developed to predict the time series of zone concentration.

Original languageEnglish
Pages (from-to)668-683
Number of pages16
JournalManagement of Environmental Quality
Issue number6
Publication statusPublished - Jun 2009


  • Forecasting
  • Industrial air pollutants
  • Kuwait
  • Neural nets
  • Ozone
  • Time series analysis

ASJC Scopus subject areas

  • Management, Monitoring, Policy and Law
  • Biochemistry, Genetics and Molecular Biology(all)

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