Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong

The objective of this study was to apply preprocessing and ensemble artificial intelligence classifiers to forecast daily maximum ozone threshold exceedances in the Hong Kong area. Preprocessing methods, including over-sampling, under-sampling, and the synthetic minority over-sampling technique, were employed to address the imbalance data problem. Ensemble algorithms are proposed to improve the classifier's accuracy. Moreover, a distance-based regional data set was generated to capture ozone transportation characteristics. The results show that a combination of preprocessing methods and ensemble algorithms can effectively forecast ozone threshold exceedances. Furthermore, this study advises on the relative importance of the different variables for ozone pollution prediction and confirms that regional data facilitate better forecasting. The results of this research can be promoted by the Hong Kong authorities for improving the existing forecasting tools. Moreover, the results can facilitate researchers' selection of the appropriate techniques in their future research. (C) 2016 Elsevier Ltd. All rights reserved.