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Application of Neural Network Based on Genetic Algorithm in Predicting Magnitude of Earthquake in North Tabriz Fault (NW Iran)


Affiliations
1 University of Tabriz, Tabriz, Iran, Islamic Republic of
 

Here we present an application of a supervised feed forward artificial neural network (ANN) that is trained on the basis of genetic algorithm (GA). The network model is used for predicting the magnitude of earthquakes in the North Tabriz Fault (NTF) North-west Iran. The earthquake database was derived from the catalogues of both the International Institute of Earthquake Engineering and Seismicity of Iran and the Iranian Seismological Center. For this purpose, three temporal seismicity parameters were calculated using the ZMAP MATLAB toolbox. The performance of the artificial neural network (ANN) model was measured in terms of accuracy by a ten-fold cross-validation as 99.11%. Another evaluation method was predicting a case event that occurred on 11 August 2012 in Ahar-Varzeghan in Iran. Results showed that the ANN optimized with GA (ANNGA) learning optimization model is suitable and may be useful for predicting future earthquakes, especially in active seismologic regions.

Keywords

Artificial Neural Network, Earthquake Magnitude, Genetic Algorithm, Temporal Seismicity Features.
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  • Hessami, K., Pantosti, D., Tabassi, H., Shabanian, E., Abbassi, M. R., Feghhi, K. and Solaymani, S., Paleoearthquakes and slip rates of the North Tabriz Fault, NW Iran: preliminary results. Ann. Geophys., 2003, 46, 903–915.

  • Gheitanchi, M. R., Mirzaei, N. and Bayramnejad, E., Pattern of seismicity in Northwest Iran, revealed from local seismic network. Geoscience, 2004, 11, 104–111.

  • Karimzadeha, S., Cakirb, Z., Osmano˘gluc, B., Schmalzled, G., Miyajimaa, M., Amiraslanzadeha, R. and Djamoure, Y., Interseismic strain accumulation across the North Tabriz Fault (NW Iran) deduced from InSAR time series. J. Geodyn., 2013, 66, 53– 58.

  • Djamour, Y., Vernant, P., Nankali, H. R. and Tavakoli, F., NW Iran-eastern Turkey present-day kinematics: results from the Iranian permanent GPS network. Earth Planet. Sci. Lett., 2011, 307, 27–34.

  • Copley, A. et al., The 2012 August 11 Ahar earthquakes: consequences for tectonics and earthquake hazard in the Turkish– Iranian Plateau. Geophys. J. Int., 2013, 196, 15–21.

  • Hoseinpour, M. and Zare, M., Seismic hazard assessment of Tabriz, a city in the northwest of Iran. J. Earth, 2009, 4, 21–35.

  • Vafaie, J., Taghikhany, T. and Tehranizade, M., Near field uniform seismic hazard spectra of Tabriz zone, presented in part at the 14th World Conference on Earthquake Engineering, China, 2008.

  • Taghizadeh-Farahmand, F., Sodoudi, F. and Afsari, N., Seismic study of upper mantle beneath the NW Iran using P receiver function. J. Earth Space Phys., 2012, 38, 17–28.

  • Vafaie, J., Taghikhany, T. and Tehranizadeh, M., Near field effect on horizontal equal-hazard spectrum of Tabriz city in north-west of Iran. Int. J. Civil Eng., 2011, 9, 49–56.

  • Bapat, A., Riddle and ridicule of earthquake prediction. Curr. Sci., 2012, 102, 554–555.

  • Baskar, R. and Baska, S., L’Aquila earthquake prediction judgment: an eye-opener. Curr. Sci., 2013, 104, 1004–1004.

  • Negarestani, A., Setayeshi, S., Ghannadi-Maragheh, M. and Akashe, B., Layered neural networks based analysis of radon concentration and environmental parameters in earthquake prediction. J. Environ. Radioactivity, 2002, 62, 225–233.

  • Zamani, A., Sorbi, M. R. and Safavi, A. A., Application of neural network and ANFIS model for earthquake occurrence in Iran. Earth Sci. Inform., 2013, 6, 71–85.

  • Jafari, M. A., Statistical prediction of the next great earthquake around Tehran, Iran. J. Geodyn., 2010, 49, 14–18.

  • Alarifia, A. S. N., Alarifib, N. S. N. and Al-Humidan, S., Earthquakes magnitude predication using artificial neural network in northern Red Sea area. J. King Saud Univ.–Sci., 2012, 24, 301– 313.

  • Moustra, M., Avraamides, M. and Christodoulou, C., Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals. Expert Syst. Appl., 2011, 38, 15032–15039.

  • Zhou, F. and Zhu, X., In Lecture Notes in Electrical Engineering, (eds Liu, X. and Ye, Y.), Springer-Verlag, Berlin, 2014, Vol. 260, pp. 13–20.

  • Niksarlioglu, S. and Kulahci, F., An artificial neural network model for earthquake prediction and relations between environmental parameters and earthquakes. Int. J. Environ., Earth Sci. Eng., 2013, 7, 31–34.

  • Adnan, A., Tiong, P. L. Y., Ismail, R. and Shamsuddin, S. M., Artificial neural network application for predicting seismic damage index of buildings in Malaysia. Electron. J. Struct. Eng., 2012, 12, 1–9.

  • Kerh, T., Huang, C. and Gunaratnam, D., Neural network approach for analyzing seismic data to identify potentially hazardous bridges. Math. Prob. Eng., 2011, 2011, 1–15.

  • Panakkat, A., Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators. Comput.-Aided Civ. Infrastruct. Eng., 2009, 24, 280–292.

  • Adeli, H. and Panakkat, A., A probabilistic neural network for earthquake magnitude prediction, Neural Networks: J. Int. Neural Network Soc., 2009, 22, 1018–1024.

  • Kulahci, F., Inceoz, M., Dogru, M., Aksoy, E. and Baykara, O., Artificial neural network model for earthquake prediction with radon monitoring. Appl. Radiat. Isotopes, 2009, 67, 212–219.

  • Zhang, Q. and Wang, C., Using genetic algorithm to optimize artificial neural network: a case study on earthquake prediction. Presented in part at the Second International Conference on Genetic and Evolutionary Computing, Jingzhou, Hubei, China, 2008, pp. 128–131.

  • Panakkat, A. and Adeli, H., Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int. J. Neural Syst., 2007, 17, 13–33.

  • Rundle, P. B., Rundle, J. B., Tiampo, K. F., Martins, J. S., McGinnis, S. and Klein, W., Nonlinear network dynamics on earthquake fault systems. Phys. Rev. Lett., 2001, 87, 148501.

  • Spassov, E., Sinadinovski, C. and McCue, K., Spatial and temporal variation of seismicity across Australia. J. Balkan Geophys. Soc., 2002, 5, 115–122.

  • Bayrak, Y. and Öztuük, S., Spatial and temporal variations of the aftershock sequences of the 1999 Izmit and Düzce earthquakes. Earth Planets Space, 2004, 56, 933–944.

  • Wiemer, S., A program to analyse seismicity: ZMAP. Geophys. Res. Lett., 2001, 72, 373–382.

  • Moradi, A. S., Hatzfeld, D. and Tatar, M., Microseismicity and seismotectonics of the North Tabriz fault (Iran), Tectonophysics, 2011, 506, 22–30.

  • Matthews, M. V. and Reasenberg, P., Statistical methods for investigating quiescence and other temporal seismicity patterns. Pageoph, 1988, 26, 357–372.

  • Reasenberg, P. A. and Simpson, R. W., Response of regional seismicity to the static stress change produced by the Loma Prieta earthquake. Science, 1992, 255, 1687–1690.

  • Habermann, R. E. and Wyss, M., Reply to ‘comment on Habermann’s method for detecting seismicity rate changes’ by M. W. Matthews and P. Reasenberg. J. Geophys. Res., 1987, 92, 9446– 9450.

  • Roy-Desrosiers, F., Package ‘ANN’. 2014; http://cran.r-project.org/ web/packages/ANN/ANN.pdf (accessed 2014).

  • R Core Team, R: a language and environment for statistical computting. R Foundation for Statistical Computing, Vienna, Austria, 2013.

  • Arinkin, V., Digel, I., Porst, D., Artmann, A. T. and Artmann, G. M., Phenotyping date palm varieties via leaflet cross-sectional imaging and artificial neural network application. BMC Bioinform., 2014, 15, 55.

  • Ghaedi, M., Zeinali, N., Ghaedi, A. M., Teimuori, M. and Tashkhourian, J., Artificial neural network-genetic algorithm based optimization for the adsorption of methylene blue and brilliant green from aqueous solution by graphite oxide nanoparticle. Spectrochim. Acta Part A, 2014, 125, 264–277.

  • Motalleb, G., Artificial neural network analysis in preclinical breast cancer. Cell J., 2014, 15, 324–331.

  • Miyajima, M. et al., Site investigation of the Ahar-Varzeghan earthquake in NW Iran of 11 August 2012. 2014; http:// committees.jsce.or.jp/disaster/system/files/FS2013-E-0001_0.pdf

  • Soleimani Zakeri, N. S. and Pashazadeh, S., Optimal number of clusters in various clustering algorithms for seismic data of Tabriz North Fault during 1950–2012 using R Software. Appl. Math. Eng., Manage. Technol. (Spl. Issue), 2014, pp. 799–809.

  • Jung, S., Sen, A. and Marron, J. S., Boundary behavior in high dimension, low sample size asymptotics of PCA. J. Multivariate Anal., 2012, 109, 190–203.


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  • Application of Neural Network Based on Genetic Algorithm in Predicting Magnitude of Earthquake in North Tabriz Fault (NW Iran)

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Authors

Negar Sadat Soleimani Zakeri
University of Tabriz, Tabriz, Iran, Islamic Republic of
Saeid Pashazadeh
University of Tabriz, Tabriz, Iran, Islamic Republic of

Abstract


Here we present an application of a supervised feed forward artificial neural network (ANN) that is trained on the basis of genetic algorithm (GA). The network model is used for predicting the magnitude of earthquakes in the North Tabriz Fault (NTF) North-west Iran. The earthquake database was derived from the catalogues of both the International Institute of Earthquake Engineering and Seismicity of Iran and the Iranian Seismological Center. For this purpose, three temporal seismicity parameters were calculated using the ZMAP MATLAB toolbox. The performance of the artificial neural network (ANN) model was measured in terms of accuracy by a ten-fold cross-validation as 99.11%. Another evaluation method was predicting a case event that occurred on 11 August 2012 in Ahar-Varzeghan in Iran. Results showed that the ANN optimized with GA (ANNGA) learning optimization model is suitable and may be useful for predicting future earthquakes, especially in active seismologic regions.

Keywords


Artificial Neural Network, Earthquake Magnitude, Genetic Algorithm, Temporal Seismicity Features.

References





DOI: https://doi.org/10.18520/cs%2Fv109%2Fi9%2F1722-1729