Forecasting of ozone pollution using artificial neural networks

Abstract

PurposeThe 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/approachAir 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.FindingsThe 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/valueA novel modelling technique is developed to predict the time series of zone concentration.

Prognosis of maximum daily surface ozone concentration within the greater Athens urban area, Greece

“…For the chosen ANN architecture, the trial-and-error method was applied (Spellman, 1999;Elkamel et al, 2001;Ettouney et al, 2009;Mahapatra, 2010) that is, after a training period, the number of PEs in the hidden layer were increased or reduced until the smallest prediction error was obtained. The selected ANN model consists of one input layer with 32 PEs, one hidden layer with 4 PEs and one output layer with 1 PE.…”

“…Thus, it is clear that an accurate ozone level prediction system will be a valuable tool. Numerous statistical and ANN models have been developed and tested in order to predict ozone concentrations (Spellman, 1999;Elkamel et al, 2001;Coman et al, 2008;Chattopadhyay and Chattopadhyay-Bandyopadhyay, 2008;Ettouney et al, 2009;Zhang et al, 2010;Mahapatra, 2010;Feng et al, 2011).…”

Towards an accurate Ground-Level Ozone Prediction

“…This comparison includes the study of seasonal trends of ozone levels, understating of behavior diurnally in ozone, assessment of effects on health by ozone pollution [11], [12].…”

“…Few studies on developing a robust system for a public warning system of forecasting that can be utilized, most of the forecasting systems were developed for the prediction of concentration in the ambient ozone in Kuwait with the use of precursor concentrations and meteorological data [11], [12].…”

Public warning systems for forecasting ambient ozone pollution in Kuwait

“…Similarly, studies that analyzed ozone levels were focused on comparing ozone levels with international standard limits, assessing health effects of ozone pollution, understanding diurnal behavior of ozone, and studying the seasonal trends in ozone levels (Ettouney et al 2009a;Ettouney et al 2009b). …”