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Cascade Feed Forward Neural Network-based Model for Air Pollutants Evaluation of Single Monitoring Stations in Urban Areas

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, air pollutants concentrations for NO2, NO, NOx and PM10 in a single monitoring station are predicted using the data coming from other different monitoring stations located nearby. A cascade feed forward neural network based modeling is proposed. The main aim is to provide a methodology leading to the introduction of virtual monitoring station points consistent with the actual stations located in the city of Catania in Italy.
Rocznik
Strony
327--332
Opis fizyczny
Bibliogr. 21 poz., il., tab., wykr.
Twórcy
autor
  • Department of Electrical, Electronics and Informatics Engineering University of Catania, Viale A. Doria 6, 95125 Catania, Italy
autor
  • Department of Electrical, Electronics and Informatics Engineering University of Catania, Viale A. Doria 6, 95125 Catania, Italy
autor
  • Department of of Biological, Geological and Environmental Science University of Catania, Catania, Italy
autor
  • Department of Mathematics and Informatics, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
Bibliografia
  • [1] C. Napoli, G. Pappalardo, E. Tramontana, Z. Marszałek, D. Połap, and M. Woźniak, “Simplified firefly algorithm for 2d image key-points search,” in Symposium on Computational Intelligence for Humanlike Intelligence (CHILI), ser. Symposium Series on Computational Intelligence (SSCI). IEEE, 2014, pp. 118-125. [Online]. Available: http://dx.doi.org/10.1109/CIHLI.2014.7013395.
  • [2] M. Woźniak, D. Połap, M. Gabryel, R. Nowicki, C. Napoli, and E. Tramontana, “Can we process 2d images using artificial bee colony?” in Artificial Intelligence and Soft Computing, ser. Lecture Notes in Computer Science. Springer International Publishing, 2015, vol. 9119, pp. 660-671, doi: 10.1007/978-3-319-19324-359.
  • [3] D. Polap, M. Wozniak, C. Napoli, E. Tramontana, and R. Damasevicius, “Is the colony of ants able to recognize graphic objects?” in Information and Software Technologies, ser. Communications in Computer and Information Science, G. Dregvaite and R. Damasevicius, Eds. Springer International Publishing, 2015, vol. 538, pp. 376-387, doi: 10.1007/978-3-319-24770-033.
  • [4] F. Bonanno, G. Capizzi, G. Lo Sciuto, C. Napoli, G. Pappalardo, and E. Tramontana, “A novel cloud-distributed toolbox for optimal energy dispatch management from renewables in igss by using wrnn predictors and gpu parallel solutions,” in International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). IEEE, 2014, pp. 1077-1084. [Online]. Available: http://dx.doi.org/10.1109/SPEEDAM.2014.6872127
  • [5] C. Napoli, G. Pappalardo, and E. Tramontana, “An agent-driven semantical identifier using radial basis neural networks and reinforcement learning,” in XV Workshop From Objects to Agents (WOA), vol. 1260. Catania, Italy: CEUR-WS, September 2014.
  • [6] K. Y. Chan and L. Jian, “Identification of significant factors for air pollution levels using a neural network based knowledge discovery system,” Neurocomputing, vol. 99, pp. 564-569, 2013.
  • [7] C. Ho, A. Knudby, P. Sirovyak, Y. Xu, M. Hodul, and S. B. Henderson, “Mapping maximum urban air temperature on hot summer days,” Remote Sensing of Environment, vol. 154, pp. 38-45, 2014.
  • [8] T. Alhanafy, F. Zaghlool, and A. El Din Moustafa, “Neuro fuzzy modeling scheme for the prediction of air pollution,” Journal of American Science, vol. 6, no. 12, pp. 605-616, 2010.
  • [9] J. Hooyberghs, C. Mensink, G. Dumont, F. Fierens, and O. Brasseur, “A neural network forecast for daily average pm10 concentrations in belgium,” Atmospheric Environment, vol. 39, no. 18, pp. 3279-3289, 2005.
  • [10] V. R. Prybutok, J. Yi, and D. Mitchell, “Comparison of neural network models with arima and regression models for prediction of houston’s daily maximum ozone concentrations,” European Journal of Operational Research, vol. 122, no. 1, pp. 31-40, 2000.
  • [11] J. Kukkonen, L. Partanen, A. Karppinen, J. Ruuskanen, H. Junninen, M. Kolehmainen, H. Niska, S. Dorling, T. Chatterton, R. Foxall, and G. Cawley, “Extensive evaluation of neural network models for the prediction of no2 and pm10 concentrations, compared with a deterministic modelling system and measurements in central helsinki,” Atmospheric Environment, vol. 37, no. 32, pp. 4539-4550, 2003.
  • [12] H. Junninen, H. Niska, K. Tuppurainen, J. Ruuskanen, and M. Kolehmainen, “Methods for imputation of missing values in air quality data sets,” Atmospheric Environment, vol. 38, no. 18, pp. 895-2907, 2004.
  • [13] M. Gardner and S. Dorling, “Neural network modelling and prediction of hourly nox and no2 concentrations in urban air in london,” Atmospheric Environment, vol. 33, no. 5, pp. 709-719, 1999.
  • [14] F. Bonanno, G. Capizzi, G. Lo Sciuto, C. Napoli, G. Pappalardo, and E. Tramontana, “A cascade neural network architecture investigating surface plasmon polaritons propagation for thin metals in openmp,” in Proceedings of International Conference on Artificial Intelligence and Soft Computing (ICAISC), ser. Springer LNCS, vol. 8467, Zakopane, Poland, June 2014, pp. 22-33.
  • [15] F. Bonanno, G. Capizzi, S. Coco, C. Napoli, A. Laudani, and G. Lo Sciuto, “Optimal thicknesses determination in a multilayer structure to improve the spp efficiency for photovoltaic devices by an hybrid fem-cascade neural network based approach,” in Proceedings of IEEE International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Ischia, Italy, June 2014, pp. 355-362.
  • [16] B. Nowak, R. Nowicki, M. Woźniak, and C. Napoli, “Multi-class nearest neighbour classifier for incomplete data handling,” in Artificial Intelligence and Soft Computing, ser. Lecture Notes in Computer Science. Springer International Publishing, 2015, vol. 9119, pp. 469-480, doi: 10.1007/978-3-319-19324-342.
  • [17] C. Napoli, G. Pappalardo, E. Tramontana, R. Nowicki, J. Starczewski, and M. Woźniak, “Toward work groups classification based on probabilistic neural network approach,” in Artificial Intelligence and Soft Computing, ser. Lecture Notes in Computer Science. Springer International Publishing, 2015, vol. 9119, pp. 79-89, doi: 10.1007/978-3-319-19324-38.
  • [18] M. Wozniak, D. Polap, R. K. Nowicki, C. Napoli, G. Pappalardo, and E. Tramontana, “Novel approach toward medical signals classifier,” in International Joint Conference on Neural Networks (IJCNN). IEEE, 2015, pp. 1924-1930, doi: 10.1109/IJCNN.2015.7280556.
  • [19] C. Napoli and E. Tramontana, “An object-oriented neural network toolbox based on design patterns,” in Information and Software Technologies, ser. Communications in Computer and Information Science, G. Dregvaite and R. Damasevicius, Eds. Springer International Publishing, 2015, vol. 538, pp. 388-399, doi: 10.1007/978-3-319-24770-034.
  • [20] S. O. Haykin, Neural Networks and Learning Machines. Upper Saddle River, New Jersery 07458: Prentice Hall, 2009 (3rd Edition).
  • [21] M. Wozniak, C. Napoli, E. Tramontana, and G. Capizzi, “A multiscale image compressor with rbfnn and discrete wavelet decomposition,” in International Joint Conference on Neural Networks (IJCNN). IEEE, 2015, pp. 1219-1225, doi: 10.1109/IJCNN.2015.7280461.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-501241ca-ee10-4ef4-84e5-ae0d89acc438
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