Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 32, No. 4 (April 2019) 467-473    Article in Press

PDF URL: http://www.ije.ir/Vol32/No4/A/2-3042.pdf  
downloaded Downloaded: 67   viewed Viewed: 347

  STATISTICAL PREDICTION OF PROBABLE SEISMIC HAZARD ZONATION OF IRAN USING SELF-ORGANIZED ARTIFICIAL INTELLIGENCE MODEL
 
A. Sivandi-Pour and E. Noroozinejad Farsangi
 
( Received: January 04, 2019 – Accepted: March 07, 2019 )
 
 

Abstract    The Iranian plateau has been known as one of the most seismically active regions of the world, and it frequently suffers destructive and catastrophic earthquakes that cause heavy loss of human life and widespread damage. Earthquakes are regularly felt on all sides of the region. Prediction of the occurrence location of the future earthquakes along with determining the probability percentage can be very useful in decreasing the seismic risks. Determining predicted locations causes increasing attention to design, seismic rehabilitation and evaluating the reliability of the present structures in these locations. No exact method has been approved for predicting future earthquake parameters yet. In recent years, more attention is paid to the earthquake magnitude prediction, but no study has been done in the field of probable earthquake occurrence hazard zonation. In this study, locations of future earthquakes in Iran were predicted by self-organized artificial neural networks (ANN). Then probable seismic risk zoning map was drawn by the statistical analyses, and the results indicated that the maps can properly predict future seismic events.

 

Keywords    Earthquake Prediction; Seismic Risk; Self-organized Artificial Neural Networks; Statistical Analysis; Zonation Map

 

چکیده   

کشور ایران به عنوان یکی از مناطق لرزه‌خیز در جهان شناخته می‌شود و اغلب زمین‌لرزه‌های مخرب و فاجعه‌بار را تجربه کرده که باعث آسیب‌های جانی و مالی زیاد شده است. پیش‌بینی موقعیت وقوع زمین‌لرزه‌های آینده همراه با تعیین درصد احتمال می‌تواند در کاهش خطرات لرزه‌ای مفید باشد. تعیین مکان پیش‌بینی شده باعث افزایش توجه به طراحی، مقاوم‌سازی لرزه‌ای و افزایش قابلیت اطمینان ساختمان‌های موجود در این مکان‌ها می‌شود. تا کنون روش دقیق و مطمئنی برای پیش‌بینی پارامترهای زلزله پیشنهاد نشده است. در سال‌های اخیر بیشتر به پیش‌بینی بزرگی زلزله توجه شده است، اما در زمینه پیش‌بینی احتمال وقوع خطر زلزله، مطالعات زیادی صورت نگرفته است. در این مطالعه، مکان زلزله‌های آینده در ایران با استفاده از شبکه‌های عصبی مصنوعی خود سازمان یافته، پیش‌بینی شده است. نقشه پهنه‌بندي احتمال خطر لرزه‌اي با استفاده از تجزيه و تحليل آماري ترسيم شد و نتايج نشان داد که نقشه‌های استخراج شده در این تحقیق مي‌توانند به طور مناسب وقايع لرزه‌اي را پيش‌بيني کنند.

References   

1. Amiri, G. G., Mahmoodi, H., and Amrei, S. A. R., “Probabilistic Seismic Hazard Assessment of Tehran Based on Arias Intensity”, International Journal of Engineering - Transactions B: Applications, Vol. 23, No. 1, (2009), 1–20. 
2. Mokhtari, M., “Earthquake prediction activities and Damavand earthquake precursor test site in Iran”, Natural Hazards, Vol. 52, No. 2, (2010), 351–368. 
3. A.Yazdani and Kowsari, M., “Statistical prediction of the sequence of large earthquakes in Iran”, International Journal of Engineering - Transactions B: Applications, Vol. 24, No. 4, (2011), 325–336. 
4. Yazdani, A., Shahpari, A., and Salimi, M. R., “The Use of Monte-Carlo Simulations in Seismic Hazard Analysis in Tehran and Surrounding Areas”, International Journal of Engineering - Transactions C: Aspects, Vol. 25, No. 2, (2012), 159–166. 
5. Yazdani, A. and Kowsari, M., “Earthquake ground-motion prediction equations for northern Iran”, Natural Hazards, Vol. 69, No. 3, (2013), 1877–1894. 
6. Raeesi, M., Zarifi, Z., Nilfouroushan, F., Amini Boroujeni, S., and Tiampo, K., “Quantitative Analysis of Seismicity in Iran”, Pure and Applied Geophysics, Vol. 174, No. 3, (2017), 793–833. 
7. Kowsari, M., Eftekhari, N., Kijko, A., Yousefi Dadras, E., Ghazi, H., and Shabani, E., “Quantifying Seismicity Parameter Uncertainties and Their Effects on Probabilistic Seismic Hazard Analysis: A Case Study of Iran”, Pure and Applied Geophysics, Vol. 176, No. 4, (2019), 1487–1502. 
8. Giardini, D., Danciu, L., Erdik, M., Şeşetyan, K., Demircioğlu Tümsa, M.B., Akkar, S., Gülen, L., and Zare, M., “Seismic hazard map of the Middle East”, Bulletin of Earthquake Engineering, Vol. 16, No. 8, (2018), 3567–3570. 
9. Ozturk, B., “Preliminary Seismic Microzonation and Seismic Vulnerability Assessment of Existing Buildings at the City of Nigde, Turkey”, In 14th World Conference on Earthquake Engineering, Beijing, China, (2008).
10. Ozturk, B., “Seismic Microzonation Studies and Vulnerability Assessment of Existing Buildings at Nigde, Turkey”, In 14th European Conference on Earthquake Engineering, Macedonia, (2010).
11. Ozturk, B., “Application of Preliminary Microzonation and Seismic Vulnerability Assessment in a City of Medium Seismic Risk in Turkey”, In 5th International Conference on Earthquake Geotechnical Engineering , Santiago, Chile, (2011).
12. Ram, T. D. and Wang, G., “Probabilistic seismic hazard analysis in Nepal”, Earthquake Engineering and Engineering Vibration, Vol. 12, No. 4, (2013), 577–586. 
13. Külahcı, F., İnceöz, M., Doğru, M., Aksoy, E., and Baykara, O., “Artificial neural network model for earthquake prediction with radon monitoring”, Applied Radiation and Isotopes, Vol. 67, No. 1, (2009), 212–219. 
14. Ashtari Jafari, M., “Statistical prediction of the next great earthquake around Tehran, Iran”, Journal of Geodynamics, Vol. 49, No. 1, (2010), 14–18. 
15. Moustra, M., Avraamides, M., and Christodoulou, C., “Artificial neural networks for earthquake prediction using time series magnitude data or Seismic Electric Signals”, Expert Systems with Applications, Vol. 38, No. 12, (2011), 15032–15039. 
16. Ni H. and Yin H., “Self-organising mixture autoregressive model for non-stationary time series modelling”, International Journal of Neural Systems, Vol. 18, No. 06, (2008), 469–480. 
17. Yang, D. and Yang, K., “Multi-step prediction of strong earthquake ground motions and seismic responses of SDOF systems based on EMD-ELM method”, Soil Dynamics and Earthquake Engineering, Vol. 85, (2016), 117–129. 
18. Yamashina, K., “Trial of earthquake prediction in Japan and a statistical test of time-shift”, Tectonophysics, Vol. 417, No. 1–2, (2006), 169–182. 
19. Borghi, A., Aoudia, A., Riva, R. E., and Barzaghi, R., “GPS monitoring and earthquake prediction: A success story towards a useful integration”, Tectonophysics, Vol. 465, No. 1–4, (2009), 177–189. 
20. Su, Y.-P. and Zhu, Q.-J., “Application of ANN to Prediction of Earthquake Influence”, In 2009 Second International Conference on Information and Computing Science, (2009), 234–237. 
21. Sharma, M. L. and Tyagi, A., “Cyclic behavior of seismogenic sources in India and use of ANN for its prediction”, Natural Hazards, Vol. 55, No. 2, (2010), 389–404. 
22. Mirrashid, M., “Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm”, Natural Hazards, Vol. 74, No. 3, (2014), 1577–1593. 
23. Chaudhuri, S., Chowdhury, A. R., and Das, P., “Implementation of Sugeno: ANFIS for forecasting the seismic moment of large earthquakes over Indo-Himalayan region”, Natural Hazards, Vol. 90, No. 1, (2018), 391–405. 
24. Kohonen, T., “The self-organizing map”, Proceedings of the IEEE, Vol. 78, No. 9, (1990), 1464–1480. 
25. AllamehZadeh, M., “Prediction of aftershocks pattern distribution using self-organising feature maps”, In 13th World Conference on Earthquake Engineering, Vol. 198, (2004).
26. Kohonen, T., “Essentials of the self-organizing map”, Neural Networks, Vol. 37, (2013), 52–65.  



Download PDF 



International Journal of Engineering
E-mail: office@ije.ir
Web Site: http://www.ije.ir