Abstract
The study described in this paper, analyzed the urban and suburban air pollution principal causes and identified the best subset of features (meteorological data and air pollutants concentrations) for each air pollutant in order to predict its medium-term concentration (in particular for the PM10). An information theoretic approach to feature selection has been applied in order to determine the best subset of features by means of a proper backward selection algorithm. The final aim of the research is the implementation of a prognostic tool able to reduce the risk for the air pollutants concentrations to be above the alarm thresholds fixed by the law. The implementation of this tool will be carried out using machine learning methods based on some of the most wide-spread statistical data driven techniques (Artificial Neural Networks, ANN, and Support Vector Machines, SVM).
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References
Benvenuto, F., Marani, A.: Neural networks for environmental problems: data quality control and air pollution nowcasting. Global NEST: The International Journal 2(3), 281–292 (2000)
Perez, P., Trier, A., Reyes, J.: Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile. Atmospheric Environment 34, 1189–1196 (2000)
Božnar, M.Z., Mlakar, P., Grašič, B.: Neural Networks Based Ozone Forecasting. In: Proc. of 9th Int. Conf. on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Garmisch-Partenkirchen, Germany (2004)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)
Goteborgs Stad Miljo: http://www.miljo.goteborg.se/luftnet/
Koller, D., Sahami, M.: Toward optimal feature selection. In: Proc. of 13th International Conference on Machine Learning (ICML), Bari, Italy, pp. 284–292 (1996)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA (1988)
Parzen, E.: On Estimation of a Probability Density Function and Mode. Annals of Math. Statistics 33, 1065–1076 (1962)
Costa, M., Moniaci, W., Pasero, E.: INFO: an artificial neural system to forecast ice formation on the road. In: Proc. of IEEE International Symposium on Computational Intelligence for Measurement Systems and Applications, pp. 216–221 (2003)
Quaderno Tecnico ARPA (Emilia Romagna) - SMR n(10) (2002)
Werbos, P.: Beyond regression: New tools for Prediction and Analysis in the Behavioural Sciences. Ph.D. Dissertation, Committee on Appl. Math. Harvard Univ. Cambridge, MA (1974)
Marquardt, D.: An algorithm for least squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431–441 (1963)
Demuth, H., Beale, M.: Neural Network Toolbox User’s Guide. The MathWorks, Inc. (2005)
Fletcher, R.: Practical Methods of Optimization, 2nd edn. John Wiley & Sons, NY (1987)
Canu, S., Grandvalet, Y., Guigue, V., Rakotomamonjy, A.: SVM and Kernel Methods Matlab Toolbox. Perception Systèmes et Information, INSA de Rouen, Rouen, France (2005), Available http://asi.insa-rouen.fr/~arakotom/toolbox/index.html
Benichou, P.: Classification automatique de configurations meteorologiques sur l’europe occidentale. Technical report. Meteo-France Monographie (1995)
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Raimondo, G., Montuori, A., Moniaci, W., Pasero, E., Almkvist, E. (2007). An Application of Machine Learning Methods to PM10 Level Medium-Term Prediction. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_32
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DOI: https://doi.org/10.1007/978-3-540-74829-8_32
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