Araştırma Makalesi
BibTex RIS Kaynak Göster

Makine öğrenimi yöntemleri kullanılarak Türkiye’nin kuzeybatısı için deprem tahmini

Yıl 2023, Cilt: 44 Sayı: 2, 166 - 178, 24.08.2023

Ayhan Doğan

https://doi.org/10.17824/yerbilimleri.1325321

Öz

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.

Anahtar Kelimeler

Makine öğrenimi Deprem tahmini Türkiye’nin kuzeybatısı odak konumu

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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.
  • Cortes, C.,Vapnik, V., 1995. Support-vector networks. Machine Learning, 20(3), 273- 297.https://doi.org/10.1007/BF00994018
  • 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
  • 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
  • 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
  • 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.
  • 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
  • Hoerl, A. E.,Kennard, R. W., 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.
  • Horel, A., 1962. Application of ridge analysis to regression problems. Chemical Engineering Progress, 58, 54-59.
  • Huang, H.,Abdel-Aty, M., 2010. Multilevel data and Bayesian analysis in traffic safety. Accident Analysis & Prevention, 42(6), 1556-1565.
  • Kirschvink, J. L., 2000. Earthquake prediction by animals: Evolution and sensory perception. Bulletin of the Seismological Society of America, 90(2), 312-323.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Montgomery, D. C., Peck, E. A.,Vining, G. G. 2021. Introduction to linear regression analysis. John Wiley & Sons, USA, 688s.
  • 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
  • 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
  • MTA. 2023. Geosciences Map Viewer. http://yerbilimleri.mta.gov.tr/anasayfa.aspx (Erişim Tarihi: 10.07.2023).
  • Natekin, A.,Knoll, A., 2013. Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21. https://doi.org/10.3389/fnbot.2013.00021
  • 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.
  • 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
  • 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
  • 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
  • Rikitake, T., 1968. Earthquake prediction. Earth-Science Reviews, 4, 245-282. https://doi.org/10.1016/0012- 8252(68)90154-2
  • 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
  • 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
  • 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
  • Soman, K., Loganathan, R.,Ajay, V. 2009. Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd., New Delhi, 477s.
  • 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
  • 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
  • USGS. 2023. Search Earthquake Catalog,. https://earthquake.usgs.gov/earthquakes/s earch (Erişim Tarihi: 08.07.2023).
  • 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
  • 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
  • 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
  • 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

Earthquake prediction for the northwest of Turkey with machine learning methods

Yıl 2023, Cilt: 44 Sayı: 2, 166 - 178, 24.08.2023

Ayhan Doğan

https://doi.org/10.17824/yerbilimleri.1325321

Öz

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.

Anahtar Kelimeler

Machine learning Earthquake prediction Northwest of Turkey Hypocenter location Hypocenter depth

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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.
  • Cortes, C.,Vapnik, V., 1995. Support-vector networks. Machine Learning, 20(3), 273- 297.https://doi.org/10.1007/BF00994018
  • 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
  • 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
  • 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
  • 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.
  • 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
  • Hoerl, A. E.,Kennard, R. W., 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.
  • Horel, A., 1962. Application of ridge analysis to regression problems. Chemical Engineering Progress, 58, 54-59.
  • Huang, H.,Abdel-Aty, M., 2010. Multilevel data and Bayesian analysis in traffic safety. Accident Analysis & Prevention, 42(6), 1556-1565.
  • Kirschvink, J. L., 2000. Earthquake prediction by animals: Evolution and sensory perception. Bulletin of the Seismological Society of America, 90(2), 312-323.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Montgomery, D. C., Peck, E. A.,Vining, G. G. 2021. Introduction to linear regression analysis. John Wiley & Sons, USA, 688s.
  • 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
  • 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
  • MTA. 2023. Geosciences Map Viewer. http://yerbilimleri.mta.gov.tr/anasayfa.aspx (Erişim Tarihi: 10.07.2023).
  • Natekin, A.,Knoll, A., 2013. Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21. https://doi.org/10.3389/fnbot.2013.00021
  • 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.
  • 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
  • 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
  • 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
  • Rikitake, T., 1968. Earthquake prediction. Earth-Science Reviews, 4, 245-282. https://doi.org/10.1016/0012- 8252(68)90154-2
  • 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
  • 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
  • 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
  • Soman, K., Loganathan, R.,Ajay, V. 2009. Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd., New Delhi, 477s.
  • 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
  • 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
  • USGS. 2023. Search Earthquake Catalog,. https://earthquake.usgs.gov/earthquakes/s earch (Erişim Tarihi: 08.07.2023).
  • 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
  • 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
  • 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
  • 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
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Sismoloji, Yer Bilimleri ve Jeoloji Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Ayhan Doğan 0000-0002-9872-8889

Yayımlanma Tarihi 24 Ağustos 2023
Gönderilme Tarihi 10 Temmuz 2023
Kabul Tarihi 15 Ağustos 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 44 Sayı: 2

Kaynak Göster

EndNote Doğan A (01 Ağustos 2023) Makine öğrenimi yöntemleri kullanılarak Türkiye’nin kuzeybatısı için deprem tahmini. Yerbilimleri 44 2 166–178.
 Kapak Resmi İndir