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Estimating the Probability of Earthquake Magnitude Between Mw = 4 and Mw = 5 for Turkey

  • Türkay Dereli
  • Cihan Çetinkaya
  • Nazmiye ÇelikEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 516)

Abstract

Earthquake is a type of disaster that occurs suddenly in different magnitudes. When magnitude of an earthquake increases it is expected that the effects are much more. Earthquakes in varying magnitude between 4 Mw and 5 Mw cause uneasiness among the public even if they do not cause heavy damage. The aim of this study is to estimate the probability of an earthquake between 4.0 and 5.0 by using artificial neural network model. Monthly real data between 2006 and 2015 is used for the model. Data is analyzed in MATLAB neural network tool, then estimated output value obtained via analysis and output of test value is compared with regression equation. Besides, seasonal effects on magnitude of earthquake are examined. Results show that 90.51% of the earthquake probability between 4.0 and 5.0 can be estimated by using artificial neural network model.

Keywords

Artificial neural network Earthquake Magnitude Seasonal effect MATLAB 

References

  1. 1.
    Florido, E., Aznarte, J., Morales-Esteban, A., Martínez-Álvarez, F.: Earthquake magnitude prediction based on artificial neural networks: a survey. Croatian Oper. Res. Rev. 7, 159–169 (2016)Google Scholar
  2. 2.
    Adeli, H., Panakkat, A.: A probabilistic neural network for earthquake magnitude prediction. Neural Netw. 22, 1018–1024 (2009)CrossRefGoogle Scholar
  3. 3.
    Stathopoulos, A., Dimitriou, L., Tsekeris, T.: Fuzzy modeling approach for combined forecasting of urban traffic flow. Comput-Aided Civil Infrastruct. Eng. 23, 521–535 (2008)CrossRefGoogle Scholar
  4. 4.
    Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Temporal evolution of short term urban traffic flow: a non-linear dynamics approach. Comput.-Aided Civil Infrastruct. Eng. 23, 536–548 (2008)CrossRefGoogle Scholar
  5. 5.
    Cichocki, A., Zdunek, R.: Multilayer nonnegative matrix factorization using projected gradient approaches. Int. J. Neural Syst. 17, 431–446 (2007)CrossRefGoogle Scholar
  6. 6.
    Kuo, H.J., Chiu, H.W., Lee, C.N., Chen, T.T., Chang, C.C., Bien, M.Y.: Improvement in the prediction of ventilator weaning outcomes by an artificial neural network in a medical ICU. Respir. Care 60, 1560–1569 (2015)CrossRefGoogle Scholar
  7. 7.
    Andrei, C.L., Oancea, B., Nedelcu, M., Sinescu, R.D.: Predicting cardiovascular diseases prevalence using neural networks. Econ. Comput. Econ. Cybern. Stud. Res. 49, 73–84 (2015)Google Scholar
  8. 8.
    Buscema, P.M., Massini, B.G., Maurelli, G.: Artificial adaptive systems to predict the magnitude of earthquakes. Bollettino di Geofisica Teorica ed Applicata 56, 227–256 (2015)Google Scholar
  9. 9.
    Alves, E.I.: Earthquake forecasting using neural networks: results and future work. Nonlinear Dyn. 44, 341–349 (2006)CrossRefGoogle Scholar
  10. 10.
    Chattopadhyay, G., Chattopadhyay, S.: Dealing with the complexity of earthquake using neurocomputing techniques and estimating its magnitude with some low correlated predictors. Arab. J. Geosci. 2, 247–255 (2009)CrossRefGoogle Scholar
  11. 11.
    Gul, M., Guneri, A.F.: An artificial neural network-based earthquake casualty estimation model for Istanbul city. Nat. Hazards 84, 2163–2178 (2016)CrossRefGoogle Scholar
  12. 12.
    Murru, M., Akinci, A., Falcone, G., Pucci, S., Console, R., Parsons, T.: M ≥ 7earthquake rupture forecast and time-dependent probability for the Sea of Marmara region, Turkey. J. Geophys. Res.: Solid Earth 121, 2679–2707 (2016)CrossRefGoogle Scholar
  13. 13.
    Asencio-Cortes, G., Martínez-Álvarez, F., Morales-Esteban, A., Troncoso, A.: Medium – large earthquake magnitude prediction in Tokyo with artificial neural networks. Neural Comput. Appl. 28, 1043–1055 (2017)CrossRefGoogle Scholar
  14. 14.
    Azam, F., Sharif, M., Yasmin, M., Mohsin, S.: Artificial intelligence based techniques for earthquake prediction: a review. Sci. Int. (Lahore) 26, 1495–1502 (2014)Google Scholar
  15. 15.
    Fawzy, D., Arslan, G.: Development of building damage functions for big earthquakes in Turkey. Procedia – Soc. Behav. Sci. 195, 2290–2297 (2015)CrossRefGoogle Scholar
  16. 16.
    Baziar, M., Ghorbani, A.: Evaluation of lateral spreading using artificial neural networks. Soil Dyn. Earthquake Eng. 25, 1–9 (2005)CrossRefGoogle Scholar
  17. 17.
    Reyes, J., Morales-Esteban, A., Martínez-Álvarez, F.: Neural networks to predict earthquakes in Chile. Appl. Soft Comput. 13, 1314–1328 (2012)CrossRefGoogle Scholar
  18. 18.
    Alarifi, A.S.N., Alarifi, N.S.N., Al-Humidan, S.: Earthquakes magnitude predication using artificial neural network in Northern Red Sea area. J. King Saud Univ. 24, 301–313 (2012)CrossRefGoogle Scholar
  19. 19.
    Li, C., Liu, X.: An improved PSO-BP neural network and its application to earthquake prediction. In: Proceedings of the Chinese Control and Decision Conference, pp. 3434–3438 (2016)Google Scholar
  20. 20.
    Zamani, A., Sorbi, M.R., Safavi, A.A.: Application of neural network and ANFIS model for earthquake occurrence in Iran. Earth Sci. Inf. 6, 71–85 (2013)CrossRefGoogle Scholar
  21. 21.
    Zhou, F., Zhu, X.: Earthquake prediction based on LM-BP neural network. Lect. Notes Electr. Eng. 270, 13–20 (2014)CrossRefGoogle Scholar
  22. 22.
    Moustra, M., Avraamides, M., Christodoulou, C.: Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals. Expert Syst. Appl. 38, 15032–15039 (2011)CrossRefGoogle Scholar
  23. 23.
    Srilakshmi, S., Tiwari, R.K.: Model dissection from earthquake time series: a comparative analysis using nonlinear forecasting and artificial neural network approach. Comput. Geosci. 35, 191–204 (2009)CrossRefGoogle Scholar
  24. 24.
    Asencio-Cortés, G., Martínez-Álvarez, F., Morales-Esteban, A., Reyes, J., Troncoso, A.: Improving earthquake prediction with principal component analysis: application to Chile. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 393–404. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19644-2_33CrossRefGoogle Scholar
  25. 25.
    Martínez-Álvarez, F., Reyes, J., Morales-Esteban, A., Rubio-Escudero, C.: Determining the Best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula. Knowl. Based Syst. 50, 198–210 (2013)CrossRefGoogle Scholar
  26. 26.
    Panakkat, A., Adeli, H.: Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int. J. Neural Syst. 17, 13–33 (2007)CrossRefGoogle Scholar
  27. 27.
    Rafiq, M.Y., Bugmann, G., Easterbrook, D.J.: Neural network design for engineering applications. Comput. Struct. 79, 1541–1552 (2001)CrossRefGoogle Scholar
  28. 28.
    Haykin, S.: Neural Networks and Learning Machines. Pearson Education Inc., Upper Saddle River (2009)Google Scholar
  29. 29.
    Kriesel, D.: A Brief Introduction to Neural Networks (ZETA2-EN) (2005)Google Scholar
  30. 30.
    Uguz, S.: Yapay Sinir Ağları-Matlab Uygulaması. https://ybssoftware.files.wordpress.com/2011/03/ysa_uygulama.pdf
  31. 31.
    RETMC.: Bogazici University Kandilli Observatory And Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center. http://www.koeri.boun.edu.tr/sismo/2/deprem-verileri/yillik-deprem-haritalari/. Accessed 21 June 2017
  32. 32.
    MathWorks.: Fit Data with a Neural Network. https://www.mathworks.com/help/nnet/gs/fit-data-with-a-neural-network.html. Accessed 21 June 2017

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Türkay Dereli
    • 1
    • 2
  • Cihan Çetinkaya
    • 3
  • Nazmiye Çelik
    • 1
    Email author
  1. 1.Industrial EngineeringGaziantep UniversityGaziantepTurkey
  2. 2.Iskenderun Technical UniversityİskenderunTurkey
  3. 3.Adana Alparslan Türkes Science and Technology UniversityAdanaTurkey

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