Forecasting of ozone pollution using artificial neural networks

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 t...

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Bibliographic Details
Published in:Management of environmental quality 2009-09, Vol.20 (6), p.668-683
Main Authors: Ettouney, Reem S, Mjalli, Farouq S, Zaki, John G, El-Rifai, Mahmoud A, Ettouney, Hisham M
Format: Article
Language:English
Subjects:
Air pollution
Environmental quality
Forecasting
Industrial air pollutants
Kuwait
Neural nets
Neural networks
Ozone
Statistical analysis
Time series
Time series analysis
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Summary: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.
ISSN:1477-7835
1758-6119
DOI:10.1108/14777830910990843