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 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.

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