Skip to main content
SpringerLink
Log in

Prediction of earthquake hazard by hidden Markov model (around Bilecik, NW Turkey)

  • Research Article
  • Published:
Central European Journal of Geosciences

Abstract

Earthquakes are one of the most important natural hazards to be evaluated carefully in engineering projects, due to the severely damaging effects on human-life and human-made structures. The hazard of an earthquake is defined by several approaches and consequently earthquake parameters such as peak ground acceleration occurring on the focused area can be determined. In an earthquake prone area, the identification of the seismicity patterns is an important task to assess the seismic activities and evaluate the risk of damage and loss along with an earthquake occurrence. As a powerful and flexible framework to characterize the temporal seismicity changes and reveal unexpected patterns, Poisson hidden Markov model provides a better understanding of the nature of earthquakes. In this paper, Poisson hidden Markov model is used to predict the earthquake hazard in Bilecik (NW Turkey) as a result of its important geographic location. Bilecik is in close proximity to the North Anatolian Fault Zone and situated between Ankara and Istanbul, the two biggest cites of Turkey. Consequently, there are major highways, railroads and many engineering structures are being constructed in this area. The annual frequencies of earthquakes occurred within a radius of 100 km area centered on Bilecik, from January 1900 to December 2012, with magnitudes (M) at least 4.0 are modeled by using Poisson-HMM. The hazards for the next 35 years from 2013 to 2047 around the area are obtained from the model by forecasting the annual frequencies of M ≥ 4 earthquakes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zucchini W. and MacDonald I. L., Hidden Markov Models for Time Series: An Introduction Using R, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Boca Raton, USA, 2009

    Book  Google Scholar 

  2. Baum L. E. and Petrie T., Statistical Inference for Probabilistic Functions of Finite State Markov Chains, The Annals of Mathematical Statistics, 37(1), 1966, 1554–1563

    Article  Google Scholar 

  3. Orfanogiannaki K., Karlis D. and Papadopoulos G. A., Identifying Seismicity Levels via Poisson Hidden Markov Models, Pure and Applied Geophysics, 167, 2010, 919–931

    Article  Google Scholar 

  4. Chambers D. W., Baglivo J. A., Ebel J. E. and Kafka A. L., Earthquake Forecasting Using Hidden Markov Models, Pure and Applied Geophysics, 169, 2012, 625–639

    Article  Google Scholar 

  5. Cappe O., Moulines E. and Ryden T., Inference in Hidden Markov Models, Springer Science+Business Media, New York, USA, 2005

    Google Scholar 

  6. MacDonald I. L. and Zucchini W., Hidden Markov Models and Other Models for Discrete-Valued Time Series, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, London, UK, 1997

    Google Scholar 

  7. Can C. E., Ergun, G., Gokceoglu C., Bilecik çevresinde deprem tehlikesinin saklı Markov modeli ile tahmini. Proceedings of the Second Turkey Earthquake Engineering and Seismology Conference, 25–27 Eylül 2013 — MKÜ — Hatay, Turkey (in Turkish), 2013

    Google Scholar 

  8. Ocakoglu F., Acikalin S., Gokceoglu C., Nefeslioglu H. A., Sonmez, H., Back-analysis of the source of the 1956 Eskisehir Earthquake using attenuation equation and damage data, Bull Eng Geol Environ, 66, 2007, 353–360

    Article  Google Scholar 

  9. UIDM, Boǧaziçi University, Kandilli Observatory and Earthquake Research Institute, Web Address: http://www.koeri.boun.edu.tr/, 2013

  10. Boore D. M., Joyner W. B., Fumal T. E., Equations for estimating horizontal response spectra and peak acceleration from western North American earthquakes: a summary of recent work, Seismol. Res. Lett., 68(1), 1997, 128–153

    Article  Google Scholar 

  11. Campbell K. W., Empirical near source attenuation relationships for horizontal and vertical components of peak ground acceleration, peak ground velocity, and pseudo-absolute acceleration response spectra, Seismol. Res. Lett., 68(1), 1997, 154–179

    Article  Google Scholar 

  12. Sadigh K., Chang C. Y., Egan J. A., Makdisi F., Youngs R. R., Attenuation relationships for shallow crustal earthquakes based on California strong motion data, Seismol. Res. Lett., 1(68), 1997, 180–189

    Article  Google Scholar 

  13. Ulusay R., Tuncay E., Sonmez H., Gokceoglu C., An attenuation relationship based on Turkish strong motion data and iso-acceleration map of Turkey, Engineering Geology, 74, 2004, 265–291

    Article  Google Scholar 

  14. Rabiner L. R., A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, 77(2), 1989, 257–285

    Article  Google Scholar 

  15. Rabiner L. R. and Juang B. -H., Fundamentals of Speech Recognition, Prentice-Hall, New Jersey, USA, 1993

    Google Scholar 

  16. Jelinek F., Statistical Methods for Speech Recognition, MIT Pres, USA, 1997

    Google Scholar 

  17. Hamilton J. D., Time Series Analysis. Princeton University Press, New Jersey, USA, 1994

    Google Scholar 

  18. Rydén T., Teräsvirta and Asbrink S., Stylized facts of daily returns series and the Hidden Markov Model, Journal of Applied Econometrics, 13, 1998, 217–244

    Article  Google Scholar 

  19. Kim C. -J. and Nelson, C. R., State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications, MIT Press, USA, 1999

    Google Scholar 

  20. A., Ruggeri F. and Soyer R., A Bayesian Hidden Markov Model for Imperfect Debugging, Reability Engineering and System Safety, 103, 2012, 11–21

    Article  Google Scholar 

  21. Landon J., Özekici S. and Soyer R., A Markov Modulated Poisson Model for Software Reability, European Journal of Operational Research, 229, 2013, 404–410

    Article  Google Scholar 

  22. Dainotti A., Pescapé A., Rossi P. S., Palmieri F., and Ventre G., Internet Traffic Modelling by Means of Hidden Markov Models, Computer Networks, 52(14), 2008, 2645–2662

    Article  Google Scholar 

  23. Leroux B. G. and Puterman M. L., Maximum-Penalized-Likelihood Estimation for Independent and Markov-Dependent Mixture Models, Biometrics, 48(2), 1992, 545–558

    Article  Google Scholar 

  24. Cave R. L. and Neuwirth L. P., Hidden Markov Models for English, In Proceedings of the Symposium on the Application of Hidden Markov Models to Text and Speech (Ferguson J. D. Editor), Princeton, New Jersey: IDA-CRD, 16–56, 1980

    Google Scholar 

  25. Charniak E., Statistical Language Learning, MIT Press, Massachusetts, 170, 1993

    Google Scholar 

  26. Zucchini W. and Guttorp P., A Hidden Markov Model for Space-Time Precipitation, Water Resources Research, 27(8), 1991, 1917–1923

    Article  Google Scholar 

  27. Sansom J., A Hidden Markov Model for Rainfall Using Breakpoint Data, Journal of Climate, 11, 1998, 42–53

    Article  Google Scholar 

  28. Bellone E., Hughes J. P. and Guttorp P., A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts, Climate Research, 15, 2000, 1–12

    Article  Google Scholar 

  29. Robertson A. W., Kirshner S. and Smyth P., Downscaling of Daily Rainfall Occurrence over Northeast Brazil Using a Hidden Markov Model, Journal of Climate, 17, 2004, 4407–4424

    Article  Google Scholar 

  30. Baldi P., Hunkapiller T., Chauvin Y. and McClure, M. A., Hidden Markov Models of Biological Primary Sequence Infromation, In Proceedings of National Academy of Science USA, Vol. 91, 1994, 1059–1063

    Article  Google Scholar 

  31. Krogh A., Brown M., Mian I. S., Sjölander K. and Haussler D., Hidden Markov models in computational biology: Applications to protein modeling, Journal of Molecular Biology, 235, 1994, 1501–1531

    Article  Google Scholar 

  32. Durbin R., Eddy S., Krogh A. and Mitchison G., Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge, England, 1998

    Book  Google Scholar 

  33. Koski T., Hidden Markov Models for Bioinformatics. Kluwer Academic Publishers, USA, 2001

    Book  Google Scholar 

  34. Fredkin D. R. and Rice J. A., Bayesian Restoration of Single-channel Patch Clamp Recordings, Biometrics, 48(2), 1992, 427–448

    Article  Google Scholar 

  35. Fredkin D. R. and Rice J. A., Maximum Likelihood Estimation and Identification Directly from Singlechannel Recordings, Proceedings: Biological Science, 249, 1992, 125–132

    Google Scholar 

  36. Ball F. G., Cai Y., Kadane J. B. and O’Hagan A., Bayesian Inference for Ion-Channel Gating Mechanisms Directly from Single-Channel Recordings, Using Markov Chain Monte Carlo. Proceedings Royal Society London A, 455, 1999, 2879–2932

    Article  Google Scholar 

  37. Granat R. and Donnellan A., A Hidden Morkov Model Based Tool for Geophysical Data Exploration, Pure and Applied Geophysics, 159(10), 2002, 2271–2283

    Article  Google Scholar 

  38. Chambers D. W., Ebel J. E., Kafka A. L. and Baglivo J. A., Hidden Markov Approach to Modeling Interevent Earthquake Times, Eos. Trans. AGU, 84(46), 2003, Fall Meet. Suppl., Abstract S52F-0179

    Google Scholar 

  39. Ebel J. E., Chambers D. W., Kafka A. L. and Baglivo J. A., Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California, Seismological Research Letters, 78(1), 2007, 57–65

    Article  Google Scholar 

  40. Orfanogiannaki K., Karlis D. and Papadopoulos G. A., Identification of Temporal Patterns in the Seismicity of Sumatra Ruprue Zone Using Poisson Hidden Markov Models, Geophysical Research Abstracts, Volume 8, 2006

  41. Orfanogiannaki K., Karlis D. and Papadopoulos G. A., Application of Poisson Hidden Markov Models on Foreshock Sequence: The case of Samos, October 2005 and Zakynthos, April 2006, Earthquakes, XIIth Int. Conf. Appl. Stochastic Models and Data Analysis, Chania, Crete, Greece, 2007

    Google Scholar 

  42. Wu Z., A Hidden Markov Model for Earthquake Declustering, Journal of Geophysical Research, 115, 2010

  43. Ephraim Y. and Merhav N., Hidden Markov Processes, IEEE Transactions On Information Theory, 48:6, 2002, 1518–1569

    Article  Google Scholar 

  44. Ghahramani Z., An Introduction to Hidden Markov Models and Bayesian Networks, International Journal of Pattern Recognition and artificial Intelligence, 15:1, 2001, 9–42

    Article  Google Scholar 

  45. Alpaydın E., Introduction to Machine Machine Learning, The MIT Press, Cambridge, USA, 2010

    Google Scholar 

  46. Ibe O. C., Markov Process for Stochasting Modelling, Elsevier Aacademic Press, California, USA, 2010

    Google Scholar 

  47. Akaike H., A New Look at the Statistical Model Identification, IEEE Transactions on Automatic Control, AC-19, 1974, 716–723

    Article  Google Scholar 

  48. Schwarz G., Estimating the Dimension of a Model, Annals of Statistics, 6, 1978, 461–464

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistics, Hacettepe University, Ankara, Turkey

    Ceren Eda Can & Gul Ergun

  2. Department of Geological Engineering, Hacettepe University, Ankara, Turkey

    Candan Gokceoglu

Authors
  1. Ceren Eda Can
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gul Ergun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Candan Gokceoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Candan Gokceoglu.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Can, C.E., Ergun, G. & Gokceoglu, C. Prediction of earthquake hazard by hidden Markov model (around Bilecik, NW Turkey). cent.eur.j.geo. 6, 403–414 (2014). https://doi.org/10.2478/s13533-012-0180-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2478/s13533-012-0180-1

Keywords

Search

Navigation