Table of Contents Author Guidelines Submit a Manuscript
Abstract and Applied Analysis
Volume 2013, Article ID 238259, 6 pages
http://dx.doi.org/10.1155/2013/238259
Research Article

Forecasting SO2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models

1Departamento de Construcción e Ingeniería de Fabricación, Universidad de Oviedo, 33203 Gijón, Spain
2Departamento de Explotación y Prospección de Minas, Universidad de Oviedo, Oviedo, 33004 Asturias, Spain
3Departamento de Estadística, Universidad de Vigo, 36310 Vigo, Spain

Received 29 October 2013; Accepted 13 November 2013

Academic Editor: L. Jódar

Copyright © 2013 Antonio Bernardo Sánchez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Hassanzadeh, F. Hosseinibalam, and R. Alizadeh, “Statistical models and time series forecasting of sulfur dioxide: a case study Tehran,” Environmental Monitoring and Assessment, vol. 155, no. 1–4, pp. 149–155, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Kandya and M. Mohan, “Forecasting the urban air quality using various statistical techniques,” in Proceedings of the 7th International Conference on Urban Climate, Yokohama, Japan, 2009.
  3. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, Calif, USA, 1976.
  4. P. Goyal, A. T. Chan, and N. Jaiswal, “Statistical models for the prediction of respirable suspended particulate matter in urban cities,” Atmospheric Environment, vol. 40, no. 11, pp. 2068–2077, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. K. M. Mok and S. C. Tam, “Short-term prediction of SO2 concentration in Macau with artificial neural networks,” Energy and Buildings, vol. 28, no. 3, pp. 279–286, 1998. View at Google Scholar · View at Scopus
  6. G. Nunnari, A. F. M. Nucifora, and C. Randieri, “The application of neural techniques to the modelling of time-series of atmospheric pollution data,” Ecological Modelling, vol. 111, no. 2-3, pp. 187–205, 1998. View at Publisher · View at Google Scholar · View at Scopus
  7. P. Pérez, A. Trier, and J. Reyes, “Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile,” Atmospheric Environment, vol. 34, no. 8, pp. 1189–1196, 2000. View at Publisher · View at Google Scholar · View at Scopus
  8. B. M. Fernández de Castro, J. M. Prada Sánchez, W. González Manteiga, M. Febrero Bande, J. L. Bermúdez Cela, and J. J. Hernández Fernández, “Prediction of SO2 levels using neural networks,” Journal of the Air & Waste Management Association, vol. 53, no. 5, pp. 532–539, 2003. View at Google Scholar
  9. S. A. Abdul-Wahab and S. M. Al-Alawi, “Prediction of sulfur dioxide (SO2) concentration levels from the Mina Al-Fahal Refinery in Oman using Artificial Neural Networks,” American Journal of Environmental Sciences, vol. 4, no. 5, pp. 473–481, 2008. View at Google Scholar · View at Scopus
  10. J. Zhang, H. Jiang, Z. Chen, X. Li, and Y. Lu, “The comparison of environmental time series statistical prediction methods,” in Proceedings of the Asia Pacific Conference on Environmental Science and Technology Advances in Biomedical Engineering, vol. 6, pp. 267–272, 2012.
  11. F.-M. Tseng, H.-C. Yu, and G.-H. Tzeng, “Combining neural network model with seasonal time series ARIMA model,” Technological Forecasting and Social Change, vol. 69, no. 1, pp. 71–87, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. L. A. Díaz-Robles, J. C. Ortega, J. S. Fu et al., “A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile,” Atmospheric Environment, vol. 42, no. 35, pp. 8331–8340, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. T. C. Mills, Time Series Techniques for Economists, Cambridge University Press, Cambridge, UK, 1990.
  14. C.-S. Ong, J.-J. Huang, and G.-H. Tzeng, “Model identification of ARIMA family using genetic algorithms,” Applied Mathematics and Computation, vol. 164, no. 3, pp. 885–912, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. R. J. Hyndman and Y. Khandakar, “Automatic time series forecasting: the forecast package for R,” Journal of Statistical Software, vol. 27, no. 3, pp. 1–22, 2008. View at Google Scholar · View at Scopus
  16. J. L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, no. 2, pp. 179–211, 1990. View at Google Scholar · View at Scopus
  17. S. Ren and L. Gao, “Resolve of overlapping voltammetric signals in using a wavelet packet transform based Elman recurrent neural network,” Journal of Electroanalytical Chemistry, vol. 586, no. 1, pp. 23–30, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989–993, 1994. View at Publisher · View at Google Scholar · View at Scopus
  19. P. G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. G. M. Ljung and G. E. P. Box, “On a measure of lack of fit in time series models,” Biometrika, vol. 65, no. 2, pp. 297–303, 1978. View at Google Scholar · View at Scopus
  21. A. C. Mathias and P. C. Rech, “Hopfield neural network: the hyperbolic tangent and the piecewise-linear activation functions,” Neural Networks, vol. 34, pp. 42–45, 2012. View at Publisher · View at Google Scholar