Comparative Analysis of Machine Learning Models for Earthquake Prediction: A Case Study of Düzce, Türkiye

Osman Dikmen

doi:10.55524/ijirem.2024.11.5.10

Abstract

This paper explores the application of machine learning models, specifically XGBoost, Stacking Regressor, and Long Short-Term Memory (LSTM), for predicting earthquake magnitudes in Düzce, Turkey. The models were trained and tested on seismic data to predict moment magnitude (Mw). The performance of each model was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicate that the XGBoost model outperforms the other models with a higher R² value and lower error metrics, providing a more accurate prediction of seismic events.

Keywords

Earthquake Prediction, LSTM, Machine Learning, Seismic Data, Stacking Regressor, XGBoost.

Reference

[1] I. M. Murwantara, P. Yugopuspito, and R. Hermawan, “Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data,” TELKOMNIKA Telecommun. Comput. Electron. Control., vol. 18, no. 3, p. 1331, Jun. 2020, Available From: http://doi.org/10.12928/telkomnika.v18i3.14756

[2] L. Sevgi, “A critical review on electromagnetic precursors and earthquake prediction,” Turkish Journal of Electrical Engineering and Computer Sciences, 2007. Available From: https://journals.tubitak.gov.tr/elektrik/vol15/iss1/1/

[3] J. H. Whitcomb, J. D. Garmany, and D. L. Anderson, “Earthquake Prediction: Variation of Seismic Velocities before the San Francisco Earthquake,” Science, vol. 180, no. 4086, pp. 632–635, May 1973, Available From: https://doi.org/10.1126/science.180.4086.632

[4] M. R. Andi Purnomo, “A Bayesian Reasoning for Earthquake Prediction Based on IoT System,” J. Phys. Conf. Ser., vol. 1471, no. 1, p. 012022, Feb. 2020, Available From: https://doi.org/10.1088/1742-6596/1471/1/012022

[5] A. Riggio and M. Santulin, “Earthquake forecasting: A review of radon as seismic precursor,” Boll. di Geofis. Teor. ed Appl., 2015, Available From: https://ricerca.ogs.it/handle/20.500.14083/1175

[6] M. Mondol, “Analysis and Prediction of Earthquakes using different Machine Learning techniques,” 35th Twente Student Conf. IT, 2021. Available From: https://essay.utwente.nl/87313/

[7] F. Azam, M. Sharif, M. Yasmin, and S. Mohsin, “Artificial Intelligence Based Techniques for Earthquake Prediction: a Review,” Sci. Int. (Lahore), 2014. Available From: https://shorturl.at/U93HB

[8] J. L. Martín Núñez and A. D. Lantada, “Artificial intelligence aided engineering education: State of the art, potentials and challenges,” Int. J. Eng. Educ., 2020. Available From: https://www.ijee.ie/1atestissues/Vol36-6/03_ijee3984.pdf

[9] J. Asefa and A. Ayele, “Seismicity of the East African Rift System for the period 2013 to 2016,” J. African Earth Sci., vol. 183, p. 104315, Nov. 2021, Available From: https://doi.org/10.1016/j.jafrearsci.2021.104315

[10] V. Gitis, A. Derendyaev, and K. Petrov, “Analyzing the Performance of GPS Data for Earthquake Prediction,” Remote Sens., vol. 13, no. 9, p. 1842, May 2021, Available From: https://doi.org/10.3390/rs13091842

[11] M. N. Brykov et al., “Machine Learning Modelling and Feature Engineering in Seismology Experiment,” Sensors, vol. 20, no. 15, p. 4228, Jul. 2020, Available From: https://doi.org/10.3390/s20154228

[12] P. Kavianpour, M. Kavianpour, E. Jahani, and A. Ramezani, “A CNN-BiLSTM model with attention mechanism for earthquake prediction,” J. Supercomput., vol. 79, no. 17, pp. 19194–19226, Nov. 2023, Available From: https://doi.org/10.1007/s11227-023-05369-y

[13] H. Alaskar and T. Saba, “Machine Learning and Deep Learning: A Comparative Review,” Springer, 2021, pp. 143–150, Available From: https://doi.org/10.1007/978-981-33-6307-6_15

[14] J. Faouzi and H. Janati, “Pyts: A python package for time series classification,” J. Mach. Learn. Res., 2020. Available From: https://www.jmlr.org/papers/v21/19-763.html

[15] L. Krischer et al., “ObsPy: a bridge for seismology into the scientific Python ecosystem,” Comput. Sci. Discov., vol. 8, no. 1, p. 014003, May 2015, Available From: https://doi.org/ 10.1088/1749-4699/8/1/014003

[16] A. Hoque, J. Raj, and A. Saha, “Approaches of Earthquake Magnitude Prediction Using Machine Learning Techniques,” SSRN Electron. J., 2018, Available From: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3513365

[17] J. W. Kim, H. Y. Joo, R. Kim, and J. H. Moon, “Investigation of the relationship between earthquakes and indoor radon concentrations at a building in Gyeongju, Korea,” Nucl. Eng. Technol., vol. 50, no. 3, pp. 512–518, Apr. 2018, Available From: https://doi.org/10.1016/j.net.2017.12.010

[18] T. Pura, P. Güne?, A. Güne?, and A. A. Hameed, “Earthquake Prediction for the Düzce Province in the Marmara Region Using Artificial Intelligence,” Appl. Sci., vol. 13, no. 15, p. 8642, Jul. 2023, Available From: https://doi.org/10.3390/app13158642

[19] A. Skelton et al., “Changes in groundwater chemistry before two consecutive earthquakes in Iceland,” Nat. Geosci., vol. 7, no. 10, pp. 752–756, Oct. 2014, Available From: https://www.nature.com/articles/ngeo2250

[20] V. Kushawaha, G. Gupta, and L. Singh, “Enhancing Energy Efficiency: Advances in Smart Grid Optimization,” Int. J. Innov. Res. Eng. Manag., vol. 11, no. 2, pp. 100–105, May 2024, Available From: https://doi.org/10.55524/ijirem.2024.11.2.20

[21] S. Dara et al., “Cardiovascular Disease Prediction Using Machine Learning Approaches,” Int. J. Innov. Res. Eng. Manag., vol. 10, no. 2, pp. 133–135, Sep. 2023, Available From: https://doi.org/10.55524/ijirem.2023.10.2.27

[22] S. Ommi and M. Hashemi, “Machine learning technique in the north zagros earthquake prediction,” Appl. Comput. Geosci., vol. 22, p. 100163, Jun. 2024, Available From: https://doi.org/10.1016/j.acags.2024.100163

[23] B. Sadhukhan, S. Chakraborty, and S. Mukherjee, “Predicting the magnitude of an impending earthquake using deep learning techniques,” Earth Sci. Informatics, vol. 16, no. 1, pp. 803–823, Mar. 2023, Available From: https://doi.org/10.1007/s12145-022-00916-2

[24] E. Abebe, H. Kebede, M. Kevin, and Z. Demissie, “Earthquakes magnitude prediction using deep learning for the Horn of Africa,” Soil Dyn. Earthq. Eng., vol. 170, p. 107913, Jul. 2023, Available From: https://doi.org/10.1016/j.soildyn.2023.107913

[25] M. Yousefzadeh, S. A. Hosseini, and M. Farnaghi, “Spatiotemporally explicit earthquake prediction using deep neural network,” Soil Dyn. Earthq. Eng., vol. 144, p. 106663, May 2021, Available From: https://doi.org/10.1016/j.soildyn.2021.106663

[26] Y. Huang, X. Han, and L. Zhao, “Recurrent neural networks for complicated seismic dynamic response prediction of a slope system,” Eng. Geol., vol. 289, p. 106198, Aug. 2021, Available From: https://doi.org/10.1016/j.enggeo.2021.106198

[27] R. Jena, B. Pradhan, S. P. Naik, and A. M. Alamri, “Earthquake risk assessment in NE India using deep learning and geospatial analysis,” Geosci. Front., vol. 12, no. 3, p. 101110, May 2021, Available From: https://doi.org/10.1016/j.gsf.2020.11.007

Cites this article as

O. Dikmen "Comparative Analysis of Machine Learning Models for Earthquake Prediction: A Case Study of Düzce, Türkiye", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-11, Issue-5, Page No-73-82, 2024. Available from: https://doi.org/10.55524/ijirem.2024.11.5.10