SavedAdd your journal

Journals / Yerbilimleri / 2026 / Cilt: 44 Sayı: 2

Earthquake prediction for the northwest of Turkey with machine learning methods

Makine öğrenimi yöntemleri kullanılarak Türkiye’nin kuzeybatısı için deprem tahmini

Ayhan Doğan(HACETTEPE ÜNİVERSİTESİ)0000-0002-9872-8889
Pages
166–178
Publication Date
2023-08-24
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The estimation results show that earthquakes of 6 or more may occur in the future in the northwestern region of Turkey, within the borders of Bursa province, starting from the north of Lake Iznik and going westwards in Ekinli, northwest of İmralı island, north of Avşa island and northwest of Marmara island.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Being able to predict earthquakes has been an important issue for human life and comfort.
📊
Key Findings & Data:In this study, the focal coordinates and focal depths of earthquakes with a magnitude of 6 and above, which are likely to occur in the northwestern region of Turkey, were tried to be estimated.
💡
Conclusion & Impact:The estimation results show that earthquakes of 6 or more may occur in the future in the northwestern region of Turkey, within the borders of Bursa province, starting from the north of Lake Iznik and going westwards in Ekinli, northwest of İmralı island, north of Avşa island and northwest of Marmara island.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Makine öğrenimi · Deprem tahmini · Türkiye’nin kuzeybatısı

Abstract

Being able to predict earthquakes has been an important issue for human life and comfort. However, earthquakes with a complex mechanism are quite difficult to predict. While different methods were used to predict earthquakes in the past, these methods have been used to make earthquake predictions with the developments in artificial intelligence methods recently. In this study, the focal coordinates and focal depths of earthquakes with a magnitude of 6 and above, which are likely to occur in the northwestern region of Turkey, were tried to be estimated. In this study, six different machine learning methods (Support Vector Machines, Linear Regression, Gradient Boost, Elastic Net, Bayesian Ridge and XGBoost) were used comparatively and the prediction results were compared. Results were evaluated with RMSE, MAE, and Adjusted R2 performance metrics. The estimation results show that earthquakes of 6 or more may occur in the future in the northwestern region of Turkey, within the borders of Bursa province, starting from the north of Lake Iznik and going westwards in Ekinli, northwest of İmralı island, north of Avşa island and northwest of Marmara island.

Özet

Depremleri önceden tahmin edebilmek insan yaşamı ve konforu için önemli bir konu olmuştur. Ancak karmaşık bir mekanizmaya sahip olan depremleri tahmin edebilmek oldukça zordur. Geçmişte depremleri tahmin edebilmek için farklı yöntemler kullanılırken son zamanlarda yapay zeka yöntemlerindeki gelişmelerle birlikte deprem tahminleri yapabilmek için bu yöntemler de kullanılmaya başlanmıştır. Bu çalışmada Türkiye’nin kuzeybatı bölgesinde gelecekte olma ihtimali bulunan, 6 ve üzeri büyüklükteki depremlerin odak koordinatları ve odak derinlikleri tahmin edilmeye çalışılmıştır. Bu çalışmada karşılaştırmalı olarak altı farklı makine öğrenimi yöntemi (Destek Vektör Makineleri, Lineer Regresyon, Gradient Boost, Elastic Net, Bayesian Ridge ve XGBoost) kullanılmış ve tahmin sonuçları karşılaştırılmıştır. Sonuçlar RMSE, MAE ve Düzeltilmiş R2 performans metrikleriyle değerlendirilmiştir. Tahmin sonuçları gelecekte Türkiye’nin kuzeybatı bölgesinde Bursa ili sınırları içerisinde İznik Gölü’nün kuzeyinden başlayarak batıya doğru Ekinli, İmralı adasının kuzeybatısı, Avşa adasının kuzeyi ve Marmara adasının kuzeybatısında 6 ve üzerinde depremler olabileceğini göstermektedir.

🔖

Cite This Article

Doğan, A. (2023). Earthquake prediction for the northwest of Turkey with machine learning methods. Yerbilimleri, Cilt: 44 Sayı: 2, 166–178. https://doi.org/10.17824/yerbilimleri.1325321

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (44)

  1. Alarifi, A. S. N., Alarifi, N. S. N.,Al-Humidan, S., 2012. Earthquakes magnitude predication using artificial neural network in northern Red Sea area. Journal of King Saud University - Science, 24(4), 301-313. https://doi.org/10.1016/j.jksus.2011.05.002
  2. Asencio-Cortés, G., Martínez-Álvarez, F., Troncoso, A.,Morales-Esteban, A., 2017. Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks. Neural Computing and Applications, 28(5), 1043-1055. https://doi.org/10.1007/s00521-015-2121-7
  3. Barsukov, V. L., Varshal, G. M.,Zamokina, N. S., 1984. Recent results of hydrogeochemical studies for earthquake prediction in the USSR. pure and applied geophysics, 122(2), 143-156. https://doi.org/10.1007/BF00874588
  4. Bhatia, M., Ahanger, T. A.,Manocha, A., 2023. Artificial intelligence based real-time earthquake prediction. Engineering Applications of Artificial Intelligence, 120, 105856. https://doi.org/10.1016/j.engappai.2023.10 5856
  5. Brehm, D. J.,Braile, L. W., 1998. Intermediate- term earthquake prediction using precursory events in the New Madrid seismic zone. Bulletin of the Seismological Society of America, 88(2), 564-580.
  6. Can, C., Ergun, G.,Gokceoglu, C., 2014. Prediction of earthquake hazard by hidden Markov model (around Bilecik, NW Turkey). Open Geosciences, 6(3), 403-414. doi:10.2478/s13533-012-0180-1
  7. Chen, T.,Guestrin, C. 2016. Xgboost: A scalable tree boosting system, Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 13-17 August, San Francisco California, 785-794.
  8. Cortes, C.,Vapnik, V., 1995. Support-vector networks. Machine Learning, 20(3), 273- 297.https://doi.org/10.1007/BF00994018
  9. De Mol, C., De Vito, E.,Rosasco, L., 2009. Elastic-net regularization in learning theory. Journal of Complexity, 25(2), 201-230. https://doi.org/10.1016/j.jco.2009.01.002
  10. Dong,W., Huang, Y., Lehane, B.,Ma, G., 2020. XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Automation in Construction, 114, 103155. https://doi.org/10.1016/j.autcon.2020.1031 55
  11. Essam, Y., Kumar, P., Ahmed, A. N., Murti, M. A.,El-Shafie, A., 2021. Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia. Soil Dynamics and Earthquake Engineering, 147, 106826. https://doi.org/10.1016/j.soildyn.2021.1068 26
  12. Evison, F., 1963. Earthquakes and faults. Bulletin of the Seismological Society of America, 53(5), 873-891. Geller, R. J., Jackson, D. D., Kagan, Y. Y.,Mulargia, F., 1997. Earthquakes cannot be predicted. Science, 275(5306), 1616-1616.
  13. Hayakawa, M.,Hobara, Y., 2010. Current status of seismo-electromagnetics for short-term earthquake prediction. Geomatics, Natural Hazards and Risk, 1(2), 115-155. https://doi.org/10.1080/19475705.2010.48 6933
  14. Hoerl, A. E.,Kennard, R. W., 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.
  15. Horel, A., 1962. Application of ridge analysis to regression problems. Chemical Engineering Progress, 58, 54-59.
  16. Huang, H.,Abdel-Aty, M., 2010. Multilevel data and Bayesian analysis in traffic safety. Accident Analysis & Prevention, 42(6), 1556-1565.
  17. Kirschvink, J. L., 2000. Earthquake prediction by animals: Evolution and sensory perception. Bulletin of the Seismological Society of America, 90(2), 312-323.
  18. Knopoff, L., 2000. The magnitude distribution of declustered earthquakes in Southern California. Proceedings of the National Academy of Sciences, 97(22), 11880-11884. https://doi.org/10.1073/pnas.190241297
  19. Külahcı, F., İnceöz, M., Doğru, M., Aksoy, E.,Baykara, O., 2009. Artificial neural network model for earthquake prediction with radon monitoring. Applied Radiation and Isotopes, 67(1), 212-219. https://doi.org/10.1016/j.apradiso.2008.08. 003
  20. Luu, Q.-H., Lau, M. F., Ng, S. P.,Chen, T. Y.,2021. Testing multiple linear regression systems with metamorphic testing. Journal of Systems and Software, 182, 111062. https://doi.org/10.1016/j.jss.2021.111062
  21. Marzocchi, W., Taroni, M.,Falcone, G., 2017. Earthquake forecasting during the complex Amatrice-Norcia seismic sequence. Science Advances, 3(9), e1701239. https://doi.org/10.1126/sciadv.1701239
  22. McCann, W. R., Nishenko, S. P., Sykes, L. R.,Krause, J., 1979. Seismic gaps and plate tectonics: Seismic potential for major boundaries. pure and applied geophysics, 117(6), 1082-1147. 10.1007/BF00876211
  23. Michael, A. J., McBride, S. K., Hardebeck, J. L., Barall, M., Martinez, E., Page, M. T., van der Elst, N., Field, E. H., Milner, K. R.,Wein,A.M., 2020. Statistical seismology andcommunication of the USGS operational aftershock forecasts for the 30 November 2018 Mw 7.1 Anchorage, Alaska, earthquake. Seismological Research Letters, 91(1), 153-173. https://doi.org/10.1785/0220190196
  24. Montgomery, D. C., Peck, E. A.,Vining, G. G. 2021. Introduction to linear regression analysis. John Wiley & Sons, USA, 688s.
  25. Moustra, M., Avraamides, M.,Christodoulou, C., 2011. Artificial neural networks for earthquake prediction using time series magnitude data or Seismic Electric Signals. Expert Systems with Applications, 38(12), 15032-15039. https://doi.org/10.1016/j.eswa.2011.05.043
  26. Möller, A., Ruhlmann-Kleider, V., Leloup, C., Neveu, J., Palanque-Delabrouille, N., Rich, J., Carlberg, R., Lidman, C.,Pritchet, C., 2016. Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning. Journal of Cosmology and Astroparticle Physics, 2016(12), 008. https://doi.org/10.1088/1475- 7516/2016/12/008
  27. MTA. 2023. Geosciences Map Viewer. http://yerbilimleri.mta.gov.tr/anasayfa.aspx (Erişim Tarihi: 10.07.2023).
  28. Natekin, A.,Knoll, A., 2013. Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21. https://doi.org/10.3389/fnbot.2013.00021
  29. Owen, A. B. 2007. A robust hybrid of lasso and ridge regression. ss 59-72. Joseph S. Verducci, Xiaotong Shen and Lafferty, J., ed. 2007. Contemporary Mathematics, 226s.
  30. Panakkat, A.,Adeli, H., 2007. Neural network models for earthquake magnitude prediction using multiple seismicity indicators. International journal of neural systems, 17(01), 13-33. https://doi.org/10.1142/S01290657070008 90
  31. Pittman, S. J.,Brown, K. A., 2011. Multi-scale approach for predicting fish species distributions across coral reef seascapes. PloS one, 6(5), e20583. https://doi.org/10.1371/journal.pone.00205 83
  32. Ricciardi, C., Ponsiglione, A. M., Scala, A., Borrelli, A., Misasi, M., Romano, G., Russo, G., Triassi, M.,Improta, G., 2022. Machine learning and regression analysis to model the length of hospital stay in patients with femur fracture. Bioengineering, 9(4), 172. https://doi.org/10.3390/bioengineering9040 172
  33. Rikitake, T., 1968. Earthquake prediction. Earth-Science Reviews, 4, 245-282. https://doi.org/10.1016/0012- 8252(68)90154-2
  34. Rouet-Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C. J.,Johnson, P. A., 2017. Machine Learning Predicts Laboratory Earthquakes. Geophysical Research Letters, 44(18), 9276-9282. https://doi.org/10.1002/2017GL074677
  35. Shi, Q., Abdel-Aty, M.,Lee, J., 2016. A Bayesian ridge regression analysis of congestion's impact on urban expressway safety. Accident Analysis & Prevention, 88, 124-137. https://doi.org/10.1016/j.aap.2015.12.001
  36. Shiuly, A., Roy, N.,Sahu, R. B., 2020. Prediction of peak ground acceleration for Himalayan region using artificial neural network and genetic algorithm. Arabian Journal of Geosciences, 13(5), 215. https://doi.org/10.1007/s12517-020-5211-5
  37. Soman, K., Loganathan, R.,Ajay, V. 2009. Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd., New Delhi, 477s.
  38. Song, K., Yan, F., Ding, T., Gao, L.,Lu, S., 2020. A steel property optimization model based on the XGBoost algorithm and improved PSO. Computational Materials Science, 174, 109472. https://doi.org/10.1016/j.commatsci.2019.1 09472
  39. Tamayo, D., Silburt, A., Valencia, D., Menou, K., Ali-Dib, M., Petrovich, C., Huang, C. X., Rein, H., Van Laerhoven, C.,Paradise, A., 2016. A machine learns to predict the stability of tightly packed planetary systems. The Astrophysical Journal Letters, 832(2), L22. https://doi.org/10.3847/2041- 8205/832/2/L22
  40. USGS. 2023. Search Earthquake Catalog,. https://earthquake.usgs.gov/earthquakes/s earch (Erişim Tarihi: 08.07.2023).
  41. Wang, K., Johnson, C. W., Bennett, K. C.,Johnson, P. A., 2022. Predicting Future Laboratory Fault Friction Through Deep Learning Transformer Models. Geophysical Research Letters, 49(19), e2022GL098233. https://doi.org/10.1029/2022GL098233
  42. Whitcomb, J. H., Garmany, J. D.,Anderson, D. L., 1973. Earthquake Prediction: Variation of Seismic Velocities before the San Francisco Earthquake. Science, 180(4086), 632-635. https://doi.org/10.1126/science.180.4086.6 32
  43. Zmazek, B., Todorovski, L., Džeroski, S., Vaupotič, J.,Kobal, I., 2003. Application of decision trees to the analysis of soil radon data for earthquake prediction. Applied Radiation and Isotopes, 58(6), 697-706. https://doi.org/10.1016/S0969- 8043(03)00094-0
  44. Zou, H.,Hastie, T., 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301-320. https://doi.org/10.1111/j.1467- 9868.2005.00503.x