Regressor selection for ozone prediction

Simulation Modelling Practice and Theory

Volumn 54, Issue , 2015, Pages 101-115

  Kocijan, Juš ^a,b   Hančič, Marko ^a   Petelin, Dejan ^a   Božnar, Marija Zlata ^c   Mlakar, Primož ^c  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF NOVA GORICA   (Slovenia)

^c MEIS doo   (Slovenia)

Author keywords

Black box modelling; Dynamical systems; Prediction of ozone concentration; Regression modelling; Regressor selection

Indexed keywords

COST FUNCTIONS; DYNAMICAL SYSTEMS; FORECASTING;

AVERAGE CONCENTRATION; BLACK BOX MODELLING; GEOGRAPHICAL LOCATIONS; OZONE CONCENTRATION; REGRESSION MODELLING; REGRESSOR SELECTION; SELECTION METHODS; TROPOSPHERIC OZONE;

OZONE;

EID: 84937763093     PISSN: 1569190X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.simpat.2015.03.004     Document Type: Article

References (35)

1. Guideline for Developing An Ozone Forecasting Program, Tech. Rep. EPA-454/R-99-009, United States Environmental Protection Agency, 1999.
2. S.M. Al-Alawi, S.A. Abdul-Wahab, and C.S. Bakheit Combining principal component regression and artificial neural-networks for more accurate predictions of ground-level ozone Environ. Model. Softw. 23 2008 396 403
3. A.B. Chelani Prediction of daily maximum ground ozone concentration using support vector machine Environ. Monit. Assess. 162 2010 169 176
4. C.-H. Cheng, S.-F. Huang, and H.-J. Teoh Predicting daily ozone concentration maxima using fuzzy time series based on a two-stage linguistic partition method Comput. Math. Appl. 2011 2016 2028
5. C. Duenas, M. Fernandez, S. Canete, J. Carretero, and E. Liger Stochastic model to forecast ground-level ozone concentration at urban and rural areas Chemosphere 61 2005 1379 1389
6. Y. Feng, W. Zhang, D. Sun, and L. Zhang Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and SVM data classification Atmos. Environ. 45 2011 1979 1985
7. T. Fontes, L. Silva, M. Silva, N. Barros, and A. Carvalho Can artificial neural networks be used to predict the origin of ozone episodes? Sci. Total Environ. 488-489 2014 197 207
8. G.G. Garner, and A.M. Thompson Ensemble statistical post-processing of the national air quality forecast capability: enhancing ozone forecasts in Baltimore Atmos. Environ. 81 2013 517 522
9. Y. Lin, and W.G. Cobourn Fuzzy system models combined with nonlinear regression for daily ground-level ozone predictions Atmos. Environ. 41 2007 3502 3513
10. K.P. Moustris, P.T. Nastos, I.K. Larissi, and A.G. Paliatsos Application of multiple linear regression models and artificial neural networks on the surface ozone forecast in the greater Athens area, Greece Adv. Meteorol. 2012 2012 1 8
11. A. Nebot, V. Mugica, and A. Escobet Ozone prediction based on meteorological variables: a fuzzy inductive reasoning approach Atmos. Chem. Phys. Discuss. 8 2008 12343 12370
12. D. Petelin, A. Grancharova, and J. Kocijan Evolving Gaussian process models for the prediction of ozone concentration in the air Simulat. Model. Pract. Theory 33 1 2013 68 80
13. T.A. Solaiman, P. Coulibaly, and P. Kanaroglou Ground-level ozone forecasting using data-driven methods Air Qual. Atmos. Health 1 2008 179 193
14. D. Sundaramoorthi A data-integrated simulation model to forecast ground-level ozone concentration Ann. Oper. Res. 216 2014 53 69
15. R.A. Hites Elements of Environmental Chemistry 2007 Wiley-Intersc. Hoboken
16. A. Bytnerowicz, K. Omasa, and E. Paoletti Integrated effects of air pollution and climate change on forests: a northern hemisphere perspective Environ. Pollut. 147 3 2006 438 445
17. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on Ambient Air Quality and Cleaner Air for Europe. .
18. R. May, G. Dandy, H. Maier, Artificial Neural Networks - Methodological Advances and Biomedical Applications, InTech, Rijeka, 2011, pp. 19-44 (Chapter Review of Input Variable Selection Methods for Artificial Neural Networks).
19. A.L. Blum, and P. Langley Selection of relevant features and examples in machine learning Artif. Intell. 97 1997 245 271
20. I. Guyon, and A. Elisseeff An introduction to variable and feature selection J. Mach. Learn. Res. 3 2003 1157 1182
21. R. Kohavi, and G.H. John Wrappers for feature subset selection Artif. Intell. 97 1-2 1997 273 324
22. I. Lind, and L. Ljung Regressor selection with the analysis of variance method Automatica 41 2005 693 700
23. M. Glavan, D. Gradišar, M. Atanasijević-Kunc, S. Strmčnik, and G. Mušič Input variable selection for model-based production control and optimisation Int. J. Adv. Manuf. Technol. 68 9-12 2013 2743 2759 http://dx.doi.org/10.1007/s00170-013-4840-1
24. G.J. Szekely, M.L. Rizzo, and N.K. Bakirov Measuring and testing dependence by correlation of distances Ann. Stat. 35 6 2007 2769 2794
25. K. Torkkola Feature extraction by non-parametric mutual information maximization J. Mach. Learn. Res. 3 2003 1415 1438
26. S. Frenzel, and B. Pompe Partial mutual information for coupling analysis of multivariate time series Phys. Rev. Lett. 99 2007 204101-1 204101-4
27. K. Li, and J.-X. Peng Neural input selection - a fast model-based approach Neurocomputation 70 1 2007 762 769
28. H. Niska, M. Heikkinen, and M. Kolehmainen Intelligent data engineering and automated learning Lecture Notes in Computer Science vol. 4224 2006 Springer 224 231 (Chapter Genetic algorithms and sensitivity analysis applied to select inputs of a multi-layer perceptron for the prediction of air pollutant time-series)
29. C.E. Rasmussen, and C.K.I. Williams Gaussian Processes for Machine Learning 2006 MIT Press Cambridge, MA
30. R.M. van Aalst, F.A.A.M. de Leeuw, National Ozone Forecasting System and International Data Exchange in Northwest Europe, European Topic Centre on Air Quality, Tech. Rep. 9, European Environment Agency, 1998.
31. P. Mlakar Determination of features for air pollution forecasting models H. Adeli (Ed.), Proceedings of Intelligent Information Systems IIS97, Grand Bahama Island, Bahamas 1997 IEEE Computer Society Los Alamitos 350 354
32. K. Ažman, and J. Kocijan Application of Gaussian processes for black-box modelling of biosystems ISA Trans. 46 2007 443 457
33. J.Q. Shi, and T. Choi Gaussian Process Regression Analysis for Functional Data 2011 Chapman and Hall/CRC, Taylor & Francis group Boca Raton, FL
34. D.J.C. MacKay Introduction to Gaussian processes C.M. Bishop (Ed.), Neural Networks and Machine Learning, NATO ASI Series 1998 Kluwer 133 166
35. J. Kocijan B. Likar Gas-liquid separator modelling and simulation with Gaussian-process models Simulat. Model. Pract. Theory 16 8 2008 910 922