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THE MODEL OF NEURAL NETWORKS FOR FORECASTING OF DISTRIBUTION OF THE AIR POLLUTION IN THE URBAN AREA
Abstract
The monitoring of quality of the urban air environment takes a big part in reaching good environmental conditions. The model of neural networks for forecasting of distribution of nitrogen dioxide in the surface layer of the air environment is shown in this research. The model is based on meteorological data and the information of sources of emissions of polluting substances. The optimal network configuration was determined on the basis of standard deviation and the quality of education. This is the cascade network with direct propagation of signal and back-propagation of error. The network consists of two layers with 11 neurons. The training of network conducted the method of LevenbergпїЅMarquardt based on environmental monitoring data. ItпїЅs shown in retrainingthat standard deviation decreases more even more than for 2 years. The results of the model are visualized in ArcGis program. The results of the modeling give the information about the distribution of air nitrogen dioxide with a regard of the different meteorological conditions and the contribution of industrial pollution in the air environment of the city, enabling a timely response and management decisions.
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. G.Ibarra-Berastegi, A. Elias, A. Barona, J. Saenz, A. Ezcurra, and J. Diaz de Argandoña ,“From diagnosis to prognosis for forecasting air pollution using neural networks: Air pollution monitoring in Bilbao”, Environmental Modelling & Software,vol. 23, no. 5, pp. 622–637, May 2008.
. T.S. Lira, M.A.S. Barrozo, and A. J. Assis, “Air quality prediction in Uberlândia, Brazil, using linear models and neural networks”,Computer Aided Chemical Engineering,vol. 24, pp. 51-56, 2007.
. D. Domańska and M. Wojtylak,“Explorative forecasting of air pollution”,Atmospheric Environment, vol. 92, pp. 19-30, August 2014.
. D. Domańska and M. Wojtylak,“Application of fuzzy time series models for forecasting pollution concentrations”,Expert Systems with Applications, vol. 39, no. 9, pp. 7673-7679, July 2012.
. H. T.Shahraiyni, S.Sodoudi, A.Kerschbaumer, and U.Cubasch,“The development of a dense urban air pollution monitoring network”,Atmospheric Pollution Research, vol. 6, no. 5, pp. 904-915, September 2015.
. G. de Gennaro, L.Trizio, A. Di Gilio, J.Pey, N. Pérez, M. Cusack, A.Alastuey, and X.Quero, “Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean” ,Science of The Total Environment,vol. 463–464, pp. 875-883, 1 October 2013.
. M.A. Elangasinghe, N. Singhal, K.N. Dirks, J.A. Salmond, and S. Samarasinghe,“Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modeling and k-means clustering”,Atmospheric Environment, vol. 94, pp. 106-116, September 2014.
. J. J. Carbajal-Hernández, L. P. Sánchez-Fernández, J. A. Carrasco-Ochoa, and J.Fco. Martínez-Trinidad,“Assessment and prediction of air quality using fuzzy logic and autoregressive models”,Atmospheric Environment,vol. 60, pp. 37-50, December 2012.
. W.Ding, J.Zhang, andY. Leung, “Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks”,Environmental Science and Pollution Research, pp.1-146, July 2016.
. K.B. Shaban, A.Kadri, and E.Rezk, “Urban air pollution monitoring system with forecasting models”,IEEE Sensors Journal.Vol. 16, no. 8, pp. 2598-2606, 15 April 2016
. S. Dursun, F.Kunt, andO.Taylan, “Modelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factors”,International Journal of Environmental Science and Technology,vol. 12, no. 12, pp. 3915-3928, 1 December 2015.
. E. Esposito, S. De Vito, M. Salvato, V. Bright, R.L. Jones, and O. Popoola,“Dynamic neural network architectures for on field stochastic calibration of indicative low cost air quality sensing systems”,Sensors and Actuators B: Chemical. 231,pp.701–713,2016.
. A. Kurt and A. B. Oktay, “Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks” Expert Systems with Applications, vol. 37, pp. 7986–7992, 2010.
. G. Zhu , P. Zhang, T. Tshukudu, J. Yin, G. Fan, and X. Zheng, “Forecasting traffic-related nitrogen oxides within a street canyon by combining a genetic algorithm-back propagation artificial neural network and parametric models” Atmospheric Pollution Research, vol. 6, pp. 1087-1097, 2015.
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