Forecasting ozone concentration levels using Box-Jenkins ARIMA modelling and artificial neural networks: A comparative study

Abstract

An accurate forecasting of tropospheric ozone (O₃) concentration is beneficial for strategic planning of air quality. In this study, various forecasting techniques are used to forecast the daily maximum O₃ concentration levels at a monitoring station in the Klang Valley, Malaysia. The Box-Jenkins autoregressive integrated moving-average (ARIMA) approach and three types of neural network models, namely, back-propagation neural network, Elman recurrent neural network and radial basis function neural network are considered. The daily maximum data, spanning from 1 January 2011 to 7 August 2011, was obtained from the Department of Environment, Malaysia. The performance of the four methods in forecasting future values of ozone concentrations is evaluated based on three criteria, which are root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The findings show that the Box-Jenkins approach outperformed the artificial neural network methods.