Abstract
This paper describes the design, development and application, of an intelligent system (operating dynamically in an iterative manner) capable of short term forecasting the concentration of dangerous air pollutants in major urban centers. This effort is the first phase of the partial fulfillment of a wider research project that is related to the development of a real time multi agent network serving the same purpose. Short term forecasting of air pollutants is necessary for the proper feed of the real time multi agent system, when one or more sensors are damaged or malfunctioning. From this point of view the potential outcome of this research is very useful towards real time air pollution monitoring. A vast volume of actual data vectors are combined from several measurement stations located in the center of Athens. The final target is the continuous estimation of Ozone (O3) in the historical city center, considering the effect of primitive pollutants and meteorological conditions from neighboring stations. A group comprising of hundreds artificial neural networks has been developed, capable of estimating effectively the concentration of O3 at a specific temporal point and also after 1, 2, 3 and 6 hours.
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References
Haykin, S.: Neural Networks A Comprehensive Foundation. Prentice Hall, USA (1999)
Iliadis, L.: Intelligent Information Systems and Applications in Risk Estimation. Hrodotos Publications (2007)
Iliadis, L., Spartalis, S., Paschalidou, A., Kassomenos: Artificial Neural Network Modeling of the surface Ozone concentration. International Journal of Computational and Applied Mathematics 2(2), 125–138 (2007)
Inal, F.: Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey. CLEAN – Soil, Air, Water 38(10), 897–908 (2010)
Jones, M.T.: AI Application Programming, 2nd edn. Thomson Delmar Learning (2005)
Kecman, V.: Learning and Soft Computing. MIT Press, USA (2001)
Ministry of Environment, Energy & Climate Change, Air Pollution Measurements (2012), http://www.ypeka.gr/Default.aspx?tabid=495&language=el-GR (valid at April 15, 2012)
Ministry of Environment, Energy & Climate Change, Air Quality, Reports, Air Pollution 2010 Annual Report (April 2011), http://www.ypeka.gr/LinkClick.aspx?fileticket=5mmiFX%2b4n2M%3d&tabid=490&language=el-GR
Ministry of Environment, Energy & Climate Change, Air Quality, Reports, Air Pollution 2009 Annual Report (2010), http://www.ypeka.gr/LinkClick.aspx?fileticket=NIpIqbQg
Ozcan, H.K., Bilgili, E., Sahin, U., Bayat, C.: Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks. Advances in Atmospheric Sciences 24(5), 907–914 (2007)
Ozdemir, H., Demir, G., Altay, G., Albayrak, S., Bayat, C.: Environmental Engineering Science 25(9), 1249–1254 (2008)
Paoli, C.: A Neural Network model forecasting for prediction of hourly ozone concentration in Corsica. In: Proceedings IEEE of the 10th International Conference on Environment and Electrical Engineering, EEEIC (2011)
Paschalidou, A., Iliadis, L., Kassomenos, P., Bezirtzoglou, C.: Neural Modeling of the Tropospheric Ozone concentrations in an Urban Site. In: Proceedings of the 10th International Conference Engineering Applications of Neural Networks, pp. 436–445 (2007)
Brian, T.J.: Methods and procedures for the verification and validation of artificial neural networks. Springer Publications (2006)
Wahab, A.-S.A., Al-Alawi, S.M.: Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environmental Modeling & Software 17, 219–228 (2002)
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Papaleonidas, A., Iliadis, L. (2012). Employing ANN That Estimate Ozone in a Short-Term Scale When Monitoring Stations Malfunction. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_8
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DOI: https://doi.org/10.1007/978-3-642-32909-8_8
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