A Type 2 Fuzzy Neural Network Ensemble to Estimate Time Increased Probability of Seismic Hazard in North Region of Baja California Peninsula

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 547)

Abstract

A type-2 adaptive fuzzy neural network ensemble approach is presented here to achieve the prediction of seismic events of M0 magnitude in the north region of the Baja California Peninsula. Three algorithms are used with the ensemble: data analysis, M8 and CN. Seismic data coordinates are used in probabilistic fuzzy sets that are processed in the three fuzzy neural networks that integrate the ensemble to generate an output of a probabilistic set of predictions.

Keywords

Fuzzy logic Neural network ensemble Seismic hazard 

References

  1. 1.
    Resnom, G.: Sismicidad de la regiónnorte de Baja California, registradaporresnom en el periodoenero-diciembre de 2002 (CICESE). Unión Geofísica Mexicana, (2002)Google Scholar
  2. 2.
    Gutenberg, B., Richter, C. F.: Seismicity of the earth and associated phenomena, 2nd edn. Princeton University Press, Princeton (1954)Google Scholar
  3. 3.
    Yang, J., Yu, P.S.: Mining asynchronous periodic patterns in time series data. IEEE Trans. Knowl. Data Eng. 15(3), 613–628 (2003)CrossRefGoogle Scholar
  4. 4.
    Kanamori, H.: Earthquake prediction: an overview. Int. Handb. Earthquake Eng. Seismolog. 616, 1205–1216 (2003)CrossRefGoogle Scholar
  5. 5.
    Karnik, N. N., Mendel, J. M.: Introduction to type-2 fuzzy logic systems. In: Proceedings of the 1998 IEEE FUZZ Conference, Anchorage, pp. 915–920 (1998)Google Scholar
  6. 6.
    Keilis-Borok, V.I.: Intermediate-term earthquake prediction (premonitory seismicity patterns/dynamics of seismicity/chaotic systems/instability). In: Proceedings of the National Academy of Sciences USA, vol. 93, pp. 3748–3755. Colloquium paper (1996)Google Scholar
  7. 7.
    Keilis-Borok, V.I., Kossobokov, V.G.: Phys. Earth Planet. Inter. 61, 73–83 (1990)CrossRefGoogle Scholar
  8. 8.
    Keilis-Borok, V.I.: The algorithm M8. Rusian academic of sciences. http://www.mitp.ru/en/m8pred.html(2009)
  9. 9.
    Main, I.: Earthquakes—long odds on prediction. Nature 385, 19–20 (1997)CrossRefGoogle Scholar
  10. 10.
    Monika, A. K.: Comparison of mamdani fuzzy model and neuro fuzzy model for load sensor. Int. J. Eng. Innovative. Technol. (IJEIT) 2(9), (2013)Google Scholar
  11. 11.
    Omori, F.: On the aftershocks of earthquakes. J Coll. Sci. 7, 111–200 (1894). Imperial University of TokyoGoogle Scholar
  12. 12.
    Pulido, M., Melin, P., Castillo, O.: Optimization of type-2 fuzzy integration in ensemble neural networks for predicting the US Dolar/MX pesos time series. IEEE (978-1-4799-0348-1 2013) (2013)Google Scholar
  13. 13.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  14. 14.
    Sepulveda, R., Castillo, O., Melin, P., Montiel, O.: An efficient computational method to implement type-2 fuzzy logic in control applications. Adv. Soft Comput. 41, 45–52 (2007)CrossRefGoogle Scholar
  15. 15.
    Sepulveda, R., Castillo, O., Melin, P., Rodriguez-Diaz, A., Montiel, O.: Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic. Inf. Sci. 177(10), 2023–2048 (2007)CrossRefGoogle Scholar
  16. 16.
    Sepulveda, R., Montiel, O., Lizarraga, G., Castillo, O.: Modeling and simulation of the defuzzification stage of a type-2 fuzzy controller using the xilinx system generator and simulink. In: Castillo, O., et al. (eds.) Studies in Computational Intelligence, vol. 257, pp. 309–325. Springer, Heidelberg (2009)Google Scholar
  17. 17.
    Sepulveda, R., Montiel, O., Castillo, O., Melin, P.: Optimizing the MFs in type-2 fuzzy logic controllers, using the human evolutionary model. Int. Rev. Autom. Control 3(1), 1–10 (2011)Google Scholar
  18. 18.
    Tsunekawa, H.: A fuzzy neural network prediction model of the principal motions of earthquakes based on preliminary tremors. IEEE (0-7803-4503-7) (1998)Google Scholar
  19. 19.
    Utsu, T.: A statistical study of the occurrence of aftershocks. Geophys. Mag. 30, 521–605 (1961)Google Scholar
  20. 20.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefMATHMathSciNetGoogle Scholar
  21. 21.
    Zamani, A., Sorbi, M.R., Safavi, A.A.: Application of neural network and ANFIS model for earthquake occurrence in Iran. Earth Sci. Inform. 6, 71–85 (2013). Springer, HeidelbergCrossRefGoogle Scholar
  22. 22.
    Zhou, Z., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artif. Intell. 137(1–2), 239–263 (2002)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyCalzada Tecnologico s/nTijuanaMexico

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