Skip to main content
SpringerLink
Log in

A New Spatial Algorithm Based on Adaptive Fuzzy Neural Network for Prediction of Crustal Motion Velocities in Earthquake Research

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

In earthquake studies, different methods are used in modeling of the crustal motions. In case of obscurity data structure, different approaches are needed in solving motion problems. In this paper, a new spatial algorithm has been developed which is based on adaptive fuzzy neural network (AFNN) approach for the prediction of the crustal motion velocities. In order to find the fuzzy class numbers regarding the network model formed by the fuzzification of the studied area, subtractive clustering algorithm is used. In determining the membership function, utilization of the variogram function which models the relationship that depends on distance among spatial data is proposed. The Marmara Region, Turkey, is used as the case for this study. In order to evaluate the performance of the approach, the kriging method is also utilized in the prediction and the results obtained from both methods are compared based on the mean-square-error criteria. It is observed that the AFNN approach yields results which are as effective as those of kriging. Consequently, it is shown that the AFNN approach will contribute to earthquake studies.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Aboonasr, S.F.G., Zamani, A., Razavipour, F., Boostani, R.: Earthquake hazard assessment in the Zagros Orogenic Belt of Iran using a fuzzy rule-based model. Acta Geophys. 65, 589–605 (2017)

    Article  Google Scholar 

  2. Ahumada, A., Altunkaynak, A., Ayoub, A.: Fuzzy logic-based attenuation relationships of strong motion earthquake records. Expert Syst. Appl. 42, 1287–1297 (2015)

    Article  Google Scholar 

  3. Alavi, A.H., Gandomi, A.M.: Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Comput. Struct. 89, 2176–2194 (2011)

    Article  Google Scholar 

  4. Alimoradi, A., Pezeshk, S., Naeim, F., Frigui, H.: Fuzzy pattern classification of strong ground motion records. J. Earthq. Eng. 9(3), 307–332 (2005)

    Google Scholar 

  5. Ameur, M., Derras, B., Zendagui, D.: Ground motion prediction model using adaptive neuro-fuzzy inference systems: an example based on the NGA-West 2 data. Pure Appl. Geophys. 175, 1–16 (2017)

    Google Scholar 

  6. Andalib, A., Zare, M., Atry, F.: A fuzzy expert system for earthquake prediction, case study: the Zagros range. Proc. Intell. Transp. Syst. 15(3), 1168–1178 (2016)

    Google Scholar 

  7. Andrić, J.M., Lu, D.-G.: Fuzzy probabilistic seismic hazard analysis with applications to Kunming city, China. Nat. Hazards 89, 1031–1057 (2017)

    Article  Google Scholar 

  8. Asencio-Cortés, G., Martínez-Álvarez, F., Troncoso, A., Morales-Esteban, A.: Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks. Neural Comput. Appl. 28, 1043–1055 (2017)

    Article  Google Scholar 

  9. Baldovino, R.G., Dadios, E.P.: A hybrid fuzzy logic–PLC-based controller for earthquake simulator system. JAC III 20, 100–105 (2016)

    Google Scholar 

  10. Bodri, B.: A neural-network model for earthquake occurrence. J. Geodyn. 32, 289–310 (2001)

    Article  Google Scholar 

  11. Chen, M.S., Wang, S.W.: Fuzzy clustering analysis for optimizing fuzzy membership functions. Fuzzy Sets Syst. 103, 239–254 (1999)

    Article  Google Scholar 

  12. Cheng, C.B., Lee, E.S.: Applying fuzzy adaptive network to fuzzy regression analysis. Comput. Math Appl. 38, 123–140 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  13. Cheng, C.B., Lee, E.S.: Fuzzy regression with radial basis function network. Fuzzy Sets Syst. 119, 291–301 (2001)

    Article  MathSciNet  Google Scholar 

  14. Cressie, N.A.C.: Statistics for spatial data. Wiley, Canada (1993)

    MATH  Google Scholar 

  15. Giacinto, G., Paolucci, R., Roli, F.: Application of neural networks and statistical pattern recognition algorithms to earthquake risk evaluation. Pattern Recogn. Lett. 18, 1353–1362 (1997)

    Article  Google Scholar 

  16. Goovaerts, P.: Geostatistics for natural resources evaluation. Oxford University Pres, New York (1997)

    Google Scholar 

  17. Huang, C., Leung, Y.: Estimating the relationship between isoseismal area and earthquake magnitude by a hybrid fuzzy-neural-network method. Fuzzy Sets Syst. 107, 131–146 (1999)

    Article  MATH  Google Scholar 

  18. Isaaks, E.H., Srivastava, R.M.: An introduction to applied geostatistics. Oxford University Pres, New York (1989)

    Google Scholar 

  19. Ishibuchi, H., Tanaka, H.: Fuzzy neural networks with fuzzy weights and fuzzy biases. In: Proceedings of 1993 IEEE International Conference on Neural Networks, San Francisco, pp. 1650–1655 (1993)

  20. Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–684 (1993)

    Article  Google Scholar 

  21. Jang, J.-S.R., Sun, C.-T.: Neuro-fuzzy modeling and control. Proc. IEEE 83(3), 378–406 (1995)

    Article  Google Scholar 

  22. Last, M., Rabinowitz, N., Leonard, G.: Predicting the maximum earthquake magnitude from seismic data in Israel and its neighboring countries. PLoS ONE 11(1), e0146 (2016)

    Article  Google Scholar 

  23. Lee, S.C., Han, S.W.: Neural-network-based models for generating artificial earthquakes and response spectra. Comput. Struct. 80, 1627–1638 (2002)

    Article  Google Scholar 

  24. Muller, S., Garda, P., Muller, J.D., Cansi, Y.: Seismic events discrimination by neuro-fuzzy merging of signal and catalogue features. Phys. Chem. Earth (A) 24(3), 201–206 (1999)

    Article  Google Scholar 

  25. Negarestani, A., Setayeshi, S., Maragheh, M.G., Akashe, B.: Estimation of the radon concentration in soil related to the environmental parameters by a modified Adaline neural network. Appl. Radiat. Isotops 58, 269–273 (2003)

    Article  Google Scholar 

  26. Reilinger, R., Mc Clusky, S., Vernant, P., Lawrence, S., Ergintav, S., Cakmak, R., Ozener, H., Kadirov, F., Guliev, I., Stepanyan, R., Nadariya, M., Hahubia, G., Mahmoud, S., Sakr, K., ArRajehi, A., Paradissis, D., Al-Aydrus, A., Prilepin, M., Guseva, T., Evren, E., Dmitrotsa, A., Filikov, S.V., Gomez, F., Al-Ghazzi, R., Karam, G.: GPS constraints on continental deformation in the Africa–Arabia–Eurasia continental collision zone and implications for the dynamics of plate interactions. J. Geophys. Res. Solid Earth 111(B5): Art. No. B05411 (2006)

  27. Rovithakis, G.A., Vallianatos, F.: A neural network approach to the identification of electric earthquake precursors. Phys. Chem. Earth (A) 25(3), 315–319 (2000)

    Article  Google Scholar 

  28. Sinclair, A.J., Blackwell, G.H.: Applied Mineral Inventory Estimation. Cambridge University Press, Weat Nyack (2002)

    Book  Google Scholar 

  29. Stein, A., Meer, F., Gorte, B.: Spatial statistics for remote sensing. Kluwer Academic, Hingham (1999)

    Google Scholar 

  30. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Trans. Syst. Man Cybernet. 15, 116–132 (1985)

    Article  MATH  Google Scholar 

  31. Tercan, A.E., ve Saraç, C.: Maden Yataklarının Değerlendirilmesinde Jeoistatistiksel Yöntemler, TMMOB Jeoloji Mühendisleri Odası Yayınları: 48, Ankara, Türkiye (1998)

  32. Tosunoğlu, N.G.: Prediction of crustal motion velocities which is constitute earthquake by the fuzzy adaptive network in spatial statistics. Ph.D. Thesis, Institute of Science, University of Ankara, Ankara, Turkey (2007) (in Turkish)

  33. Wyss, M.: Why is earthquake prediction research not progressing faster? Tectonophysics 338, 217–223 (2001)

    Article  Google Scholar 

  34. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  MATH  Google Scholar 

  35. Zhang, J., Yao, N.: The geostatistical framework for spatial prediction. Geo-Spatial Inf. Sci. 11(3), 180–185 (2008)

    Article  Google Scholar 

  36. Zhong, M., Zhang, Q.: Prediction of reservoir-induced earthquake based on fuzzy theory. In: Proceedings of the Second International Symposium on Networking and Network Security (ISNNS’10), Jinggangshan, pp. 101–104 (2010)

Download references

Author information

Authors and Affiliations

  1. Department of International Trade, Gazi University, 06500, Ankara, Turkey

    Nuray Güneri Tosunoğlu

  2. Department of Insurance and Actuarial Science, Ankara University, 06532, Ankara, Turkey

    Ayşen Apaydın

Authors
  1. Nuray Güneri Tosunoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayşen Apaydın
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nuray Güneri Tosunoğlu.

Additional information

This paper is based on the  Ph.D. thesis of Dr. Nuray Güneri Tosunoğlu [32].

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Güneri Tosunoğlu, N., Apaydın, A. A New Spatial Algorithm Based on Adaptive Fuzzy Neural Network for Prediction of Crustal Motion Velocities in Earthquake Research. Int. J. Fuzzy Syst. 20, 1656–1670 (2018). https://doi.org/10.1007/s40815-018-0483-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-018-0483-6

Keywords

Search

Navigation