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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/20148

Title: Modelling ground-level ozone concentration using ensemble learning algorithms
Authors: Al Abri, Eman S.
Edirisinghe, Eran A.
Nawadha, Amin
Keywords: Ozone
Atmospheric pollution
Machine learning
Environment science
Ensemble classifiers
Issue Date: 2015
Publisher: World Academy of Science
Citation: AL ABRI, E.S., EDIRISINGHE, E.A. and NAWADHA, A., 2015. Modelling ground-level ozone concentration using ensemble learning algorithms. Proceedings of the International Conference on Data Mining (DMIN), 27th-30th July 2015, Las Vegas, USA, pp.148-154
Abstract: Environmental risks caused by exposure to ground level ozone have significantly increased during recent years. One main producer of ozone is the photochemical reaction between volatile organic components and the anthropogenic nitrogen oxides created by vehicular traffic. Therefore the measurement and monitoring of atmospheric ozone concentration levels is important. In this paper we propose a study of the use of state-of-the-art machine learning approaches in modelling the concentration of ground level ozone. 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 analysis of the results indicates that accurate models for the concentration of ground level ozone can be derived with the best performance accuracies indicated by the Ensemble Learning Algorithms. The investigation carried out compares the use of different machine learning classifiers and show that the Ensembleclassifier 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 standard learning algorithms such as Neural Networks and Support Vector Machines often available within popular commercial software packages.
Description: This is a conference paper. It is also available online at: http://worldcomp-proceedings.com/proc/p2015/DMI8031.pdf
Version: Published
URI: https://dspace.lboro.ac.uk/2134/20148
Publisher Link: http://worldcomp-proceedings.com/proc/p2015/DMI8031.pdf
Appears in Collections:Conference Papers and Presentations (Computer Science)

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