In this paper we propose a study of the use of stateof- the-art machine learning approaches in modelling the concentration of ground level ozone for air pollution monitoring. The prediction is based on concentrations of seven gases (NO2, SO2, and BTX (Benzene, Toluene, o-,m- ,p-Xylene) and six meteorological parameters (ambient temperature, air pressure, wind speed, wind direction, global radiation, and relative humidity). The investigation carried outcompares the use of different machine learning classifiers and show that the Ensemble-classifier Bagging performs superior to standard single classifiers, such as Artificial Neural Networks and Support Vector Machines, popularly used in literature. In addition, we study the performance of the meta-classifier Bagging, when different base classifiers are used in optimised configurations and compare the results thus obtained. The research conducted bridges an existing research gap in big-data analytics related to environment pollution prediction, where present research is largely limited to using traditional learning algorithms such as Neural Networks and Support Vector Machines.

In this paper we propose a study of the use of stateof- the-art machine learning approaches in modelling the concentration of ground level ozone for air pollution monitoring. The prediction is based on concentrations of seven gases (NO2, SO2, and BTX (Benzene, Toluene, o-,m- ,p-Xylene) and six ...