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Supervised Metaplasticity for Big Data: Application to Pollutant Concentrations Forecast

  • J. FombellidaEmail author
  • M. J. Alarcon
  • S. Torres-Alegre
  • D. Andina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)

Abstract

Artificial Metaplasticity Multilayer Perceptron is a training algorithm implementation for Artificial Neural Networks inspired in biological metaplasticity property of neurons and Shannon’s information theory. It is based on the hypothesis that a higher amount of information from a Data Set is included in the most atypical data. Using this theory basis a supervised algorithm is developed giving more relevance to the less frequent patterns and subtracting relevance to the more frequent ones. This algorithm has achieved deeper learning on several mutidisciplinar data sets without the need of a Deep Network. The application of this algorithm to a key nowadays environmental problem: the pollutant concentrations prediction in cities, is now considered. The city selected is Salamanca, Mexico, that has been ranked as one of the most polluted cities in the world. The concerning registered pollutants are particles in the order of 10 \(\upmu \)m or less (\(PM_{10}\)). The prediction of concentrations of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this paper the results obtained are compared with previous recent published algorithms for the prediction of the pollutant concentration. Discussed and conclusions are presented.

Keywords

Metaplasticity Big Data Plasticity MLP AMP Pollutant concentration Artificial neural network 

References

  1. 1.
    U.S.EPA: U.S. Environmental Protection Agency (2012). www.epa.gov/air/airpollutants.html
  2. 2.
    SESA: Spanish acronym for Spanish Society of Environmental Health (2008). http://www.sanidadambiental.com/2008/08/19/environment-and-health/
  3. 3.
    Andina, D., Ropero-Pelaez, J.: On the biological plausibility of artificial metaplasticity learning algorithm. Neurocomputing (2012). doi: 10.1016/j.neucom.2012.09.028 Google Scholar
  4. 4.
    Andina, D., Alvarez-Vellisco, A., Jevtic, A., Fombellida, J.: Artificial metaplasticity can improve artificial neural network learning. Intell. Autom. Soft Comput. Spec. Issue Signal Process. Soft Comput. 15(4), 681–694 (2009)Google Scholar
  5. 5.
    Barron-Adame, J.M., Cortina-Januchs, M.G., Vega-Corona, A., Andina, D.: Unsupervised system to classify \(SO_{2}\) pollutant concentrations in Salamanca, Mexico. Expert Syst. Appl. 39, 107–116 (2012)CrossRefGoogle Scholar
  6. 6.
    Cortina-Januchs, M.G., Quintanilla-Dominguez, J., Vega-Corona, A., Andina, D.: Development of a model for forecasting of \(PM_{10}\) concentrations in Salamanca, Mexico. Atmos. Pollut. Res. 6, 626–634 (2015). doi: 10.5094/APR.2015.071 CrossRefGoogle Scholar
  7. 7.
    Celik, M., Kadi, I.: The relation between meteorological factors and pollutants concentration in Karabuk city. G.U. J. Sci. 20, 89–95 (2007)Google Scholar
  8. 8.
    D’Amato, G., Cecchi, L., D’Amato, M., Liccardi, G.: Urban air pollution and climate change as environmental risk factors of respiratory allergy: an update. J. Investig. Allergol. Clin. Immunol. 20, 95–102 (2010)Google Scholar
  9. 9.
    Elminir, H.K.: Dependence of urban air pollutants on meteorology. Sci. Total Environ. 350, 225–237 (2005)CrossRefGoogle Scholar
  10. 10.
    Fombellida, J., Torres-Alegre, S., Piñuela-Izquierdo, J.A., Andina, D.: Artificial metaplasticity for deep learning: application to WBCD breast cancer database classification. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo-Moreo, F.J., Adeli, H. (eds.) IWINAC 2015. LNCS, vol. 9108, pp. 399–408. Springer, Cham (2015). doi: 10.1007/978-3-319-18833-1_42 CrossRefGoogle Scholar
  11. 11.
    Lee, S.H., Sung, Y.H., Lee, H.W.: Impact of regional trans-boundary ozone associated with complex terrain on urban air quality. Atmos. Environ. 42, 7384–7396 (2008)CrossRefGoogle Scholar
  12. 12.
    Nagendra, S.M.S., Khare, M.: Artificial neural network based line source models for vehicular exhaust emission predictions of an urban roadway. Transp. Res. Part D-Transp. Environ. 9, 199–208 (2004)CrossRefGoogle Scholar
  13. 13.
    Pearce, J.L., Beringer, J., Nicholls, N., Hyndman, R.J., Tapper, N.J.: Quantifying the influence of local meteorology on air quality using generalized additive models. Atmos. Environ. 45, 1328–1336 (2011)CrossRefGoogle Scholar
  14. 14.
    Perez, P., Trier, A., Reyes, J.: Prediction of \(PM_{2.5}\) concentrations several hours in advance using neural networks in Santiago, Chile. Atmos. Environ. 34, 1189–1196 (2000)CrossRefGoogle Scholar
  15. 15.
    Ropero-Pelaez, J., Andina, D.: Do biological synapses perform probabilistic computations? Neurocomputing (2012). http://dx.doi.org/10.1016/j.neucom.2012.08.042

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Group for Automation in Signals and CommunicationsUniversidad Politécnica de MadridMadridSpain

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