Evaluation of Clustering Algorithms in Earthquake Magnitude Prediction: An Adaptive Neuro-Fuzzy Inference System-Based Study
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Published
Apr 26, 2026
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Abstract
Earthquakes are natural disasters that can cause significant loss of life and property, and accurate prediction of earthquake parameters is of great importance in earthquake engineering. This study investigates the effect of different clustering algorithms on Adaptive Neuro-Fuzzy Inference System (ANFIS)-based models in predicting earthquake magnitude. The study used 374 earthquake records obtained from the AFAD Turkey Acceleration Database and Analysis System (TADAS). The dataset was divided into 340 training and 34 test data. Model inputs were defined as year, earthquake depth, and peak ground acceleration (PGA), and the model output was moment magnitude (Mw). K-means, hierarchical clustering, DBSCAN, and Gaussian Mixture Model (GMM) algorithms were used to determine the data structure. The performance of the generated ANFIS models was evaluated using R², RMSE, and MAE metrics. The results show that model configurations created with GMM-based clustering provide higher prediction accuracy.
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How to Cite
SÜRÜCÜ, Burak; UZUNDURUKAN, Soner.
Evaluation of Clustering Algorithms in Earthquake Magnitude Prediction: An Adaptive Neuro-Fuzzy Inference System-Based Study.
Journal of Multidisciplinary Developments, [S.l.], v. 11, n. 1, p. 9-26, apr. 2026.
ISSN 2564-6095.
Available at: <http://jomude.com/index.php/jomude/article/view/126>. Date accessed: 13 may 2026.
Section
Natural Sciences - Regular Research Paper
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References
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[39] Walia, N., Singh, H., & Sharma, A. (2015). ANFIS: Adaptive neuro-fuzzy inference system-a survey. International Journal of Computer Applications, 123(13).
[40] Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82.
[41] Xu, D., & Tian, Y. (2015). A comprehensive survey of clustering algorithms. Annals of data science, 2(2), 165-193.
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[2] Azar, A. T. (Ed.). (2010). Fuzzy systems. BoD–Books on Demand.
[3] Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
[4] Bozkurt, E. (2001). Neotectonics of Turkey–a synthesis. Geodinamica acta, 14(1-3), 3-30.
[5] Cabalar, A. F., Cevik, A., & Gokceoglu, C. (2012). Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Computers and Geotechnics, 40, 14-33.
[6] Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250.
[7] Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7, e623.
[8] Deng, D. (2020, September). DBSCAN clustering algorithm based on density. In 2020 7th international forum on electrical engineering and automation (IFEEA) (pp. 949-953). IEEE.
[9] Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38.
[10] Disaster and Emergency Management Authority (AFAD). (2025). Turkish Strong Motion Database (TADAS). https://tadas.afad.gov.tr
[11] Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd (Vol. 96, No. 34, pp. 226-231).
[12] Ferraro, M. B., & Giordani, P. (2020). Soft clustering. Wiley Interdisciplinary Reviews: Computational Statistics, 12(1), e1480.
[13] Hanks, T. C., & Kanamori, H. (1979). A moment magnitude scale. Journal of Geophysical Research: Solid Earth, 84(B5), 2348-2350.
[14] Harchaoui, Z., Moulines, E., & Bach, F. (2008). Kernel change-point analysis. Advances in neural information processing systems, 21.
[15] Ienco, D., & Bordogna, G. (2018). Fuzzy extensions of the DBScan clustering algorithm. Soft Computing, 22(5), 1719-1730.
[16] Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666.
[17] Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
[18] Kamath, R. S., & Kamat, R. K. (2017). Earthquake magnitude prediction for Andaman-Nicobar Islands: Adaptive neuro fuzzy modeling with fuzzy subtractive clustering approach. J. Chem. Pharm. Sci, 10(3), 1228-1233.
[19] Kanamori, H. (1977). The energy release in great earthquakes. Journal of geophysical research, 82(20), 2981-2987.
[20] Kong, Q., Trugman, D. T., Ross, Z. E., Bianco, M. J., Meade, B. J., & Gerstoft, P. (2019). Machine learning in seismology: Turning data into insights. Seismological research letters, 90(1), 3-14.
[21] Kose, U., & Arslan, A. (2017). Forecasting chaotic time series via anfis supported by vortex optimization algorithm: Applications on electroencephalogram time series. Arabian Journal for Science and Engineering, 42(8), 3103-3114.
[22] Maheswaran, G. (2013). K Means Clustering Algorithms: A Comparitive Study.
[23] Mimmack, G. M., Mason, S. J., & Galpin, J. S. (2001). Choice of distance matrices in cluster analysis: Defining regions. Journal of climate, 14(12), 2790-2797.
[24] Mirrashid, M. (2014). Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm. Natural hazards, 74(3), 1577-1593.
[25] Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature communications, 11(1), 3952.
[26] Murtagh, F., & Contreras, P. (2012). Algorithms for hierarchical clustering: an overview. Wiley interdisciplinary reviews: data mining and knowledge discovery, 2(1), 86-97.
[27] Nielsen, F. (2016). Hierarchical clustering. In Introduction to HPC with MPI for Data Science (pp. 195-211). Cham: Springer International Publishing.
[28] Noor, S. Q., & Mustafa, T. K. (2024). Comparing K-Means, Nearest Neighbor, and Lloyd's Clustering Algorithms. Iraqi Journal of Science, 6688-6704.
[29] Nurlu, M., Fahjan, Y., Eravcı, B., Baykal, M., Yenilmez, G., Yalçın, D., ... & Pakdamar, F. (2014, August). Rapid estimation of earthquake losses in Turkey using AFAD-RED system. In 2nd European conference on earthquake engineering and seismology, Istanbul.
[30] Ozer, D. J. (1985). Correlation and the coefficient of determination. Psychological bulletin, 97(2), 307.
[31] Pandit, A., & Biswal, K. C. (2019). Prediction of earthquake magnitude using adaptive neuro fuzzy inference system. Earth Science Informatics, 12(4), 513-524.
[32] Riddell, R. (1979). Statistical analysis of the response of nonlinear systems subjected to earthquakes. University of Illinois at Urbana-Champaign.
[33] Salleh, M. N. M., Talpur, N., & Hussain, K. (2017, June). Adaptive neuro-fuzzy inference system: Overview, strengths, limitations, and solutions. In International conference on data mining and big data (pp. 527-535). Cham: Springer International Publishing.
[34] Saplıoğlu, K., & Acar, R. (2020). K-means kümeleme algoritması kullanılarak oluşturulan yapay zeka modelleri ile sediment taşınımının tespiti. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9(1), 306-322.
[35] Saplıoğlu, K., & Uzundurukan, S. (2019). Bilimsel çalışmalarda kullanılan bazı yapay zeka uygulamalarının ve trendlerinin incelenmesi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 10(1), 249-262.
[36] Shearer, P. M. (2019). Introduction to seismology. Cambridge university press.
[37] Şengör, A. M. C., Görür, N., & Şaroğlu, F. (1985). Strike-slip faulting and related basin formation in zones of tectonic escape: Turkey as a case study.
[38] The MathWorks Inc. (2026). MATLAB (Version R2023b) [Computer software]. Natick, MA, USA.
[39] Walia, N., Singh, H., & Sharma, A. (2015). ANFIS: Adaptive neuro-fuzzy inference system-a survey. International Journal of Computer Applications, 123(13).
[40] Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82.
[41] Xu, D., & Tian, Y. (2015). A comprehensive survey of clustering algorithms. Annals of data science, 2(2), 165-193.
[42] Zhang, Y., Li, M., Wang, S., Dai, S., Luo, L., Zhu, E., ... & Zhou, H. (2021). Gaussian mixture model clustering with incomplete data. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(1s), 1-14.