165.
The present paper proposes a wavelet based recurrent neural network model to forecast one step ahead hourly, daily mean and
daily maximum concentrations of ambient CO, NO
2, NO, O
3, SO
2 and PM
2.5 — the most prevalent air pollutants in urban atmosphere. The time series of each air pollutant has been decomposed into different
time-scale components using maximum overlap wavelet transform (MODWT). These time-scale components were made to pass through
Elman network. The number of nodes in the network was decided on the basis of the strength (power) of the corresponding input
signals. The wavelet network model was then used to obtain one-step ahead forecasts for a period extending from January 2009
to June 2010. The model results for out of sample forecast are reasonably good in terms of model performance parameters such
as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized mean absolute
error (NMSE), index of agreement (IOA) and standard average error (SAE). The MAPE values for daily maximum concentrations
of CO, NO
2, NO, O
3, SO
2 and PM2.5 were found to be 9.5%, 17.37%, 21.20%, 13.79%, 17.77% and 11.94%, respectively, at ITO, Delhi, India. Bearing in
mind that the forecasts are for daily maximum concentrations tested over a long validation period, the forecast performance
of the model may be considered as reasonably good. The model results demonstrate that a judicious selection of wavelet network
design may be employed successfully for air quality forecasting.
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