Evolving Gaussian process models for prediction of ozone concentration in the air

Abstract Ozone is one of the main air pollutants with harmful influence to human health. Therefore, predicting the ozone concentration and informing the population when the air-quality standards are not being met is an important task. In this paper, various first- and high-order Gaussian process models for prediction of the ozone concentration in the air of Bourgas, Bulgaria are identified off-line based on the hourly measurements of the concentrations of ozone, sulphur dioxide, nitrogen dioxide, phenol and benzene in the air and the meteorological parameters, collected at the automatic measurement stations in Bourgas. Further, as an alternative approach an on-line updating (evolving) Gaussian process model is proposed and evaluated. Such an approach is needed when the training data is not available through the whole period of interest and consequently not all characteristics of the period can be trained or when the environment, that is to be modelled, is constantly changing.

[1]  W. Geoffrey Cobourn,et al.  Fuzzy system models combined with nonlinear regression for daily ground-level ozone predictions , 2007 .

[2]  Dezhi Sun,et al.  Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification , 2011 .

[3]  Lehel Csató,et al.  Sparse On-Line Gaussian Processes , 2002, Neural Computation.

[4]  Juö Kocijan,et al.  Gaussian Process Models for Systems Identification , 2008 .

[5]  Saleh M. Al-Alawi,et al.  Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone , 2008, Environ. Model. Softw..

[6]  A. Chelani,et al.  Prediction of daily maximum ground ozone concentration using support vector machine , 2010, Environmental monitoring and assessment.

[7]  Paulin Coulibaly,et al.  Ground-level ozone forecasting using data-driven methods , 2008 .

[8]  Jus Kocijan,et al.  Control system with evolving Gaussian process models , 2011, 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS).

[9]  Karl Johan Åström,et al.  Computer-Controlled Systems: Theory and Design , 1984 .

[10]  Neil D. Lawrence,et al.  Fast Forward Selection to Speed Up Sparse Gaussian Process Regression , 2003, AISTATS.

[11]  Marcello Farina,et al.  Forecasting peak air pollution levels using NARX models , 2009, Eng. Appl. Artif. Intell..

[12]  Nikola Kasabov,et al.  Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines , 2002, IEEE Transactions on Neural Networks.

[13]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[14]  Plamen Angelov,et al.  Evolving Intelligent Systems: Methodology and Applications , 2010 .

[15]  Tor Arne Johansen,et al.  Explicit stochastic predictive control of combustion plants based on Gaussian process models , 2008, Autom..

[16]  P. Angelov,et al.  Evolving rule-based models: A tool for intelligent adaptation , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[17]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[18]  Agathe Girard,et al.  Dynamic systems identification with Gaussian processes , 2005 .

[19]  J Kocijan,et al.  Application of Gaussian processes for black-box modelling of biosystems. , 2007, ISA transactions.

[20]  Bojan Likar,et al.  Gas-liquid separator modelling and simulation with Gaussian-process models , 2008, Simul. Model. Pract. Theory.

[21]  Carl E. Rasmussen,et al.  A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..

[22]  J. Carretero,et al.  Stochastic model to forecast ground-level ozone concentration at urban and rural areas. , 2005, Chemosphere.

[23]  J. Weston,et al.  Approximation Methods for Gaussian Process Regression , 2007 .

[24]  Àngela Nebot,et al.  Ozone prediction based on meteorological variables: a fuzzy inductive reasoning approach , 2008 .