Predicting the Earthquake Magnitude along Zagros Fault Using Time Series and Ensemble Model

Document Type : Regular Article


1 Ph.D. Candidate, Environmental Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

2 Research Assistant, Geotechnical Earthquake Engineering, International Institute of Earthquake Engineering and Seismology, Tehran, Iran

3 Master of Science Student, Structural Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

4 Master of Science Student, Structural Engineering, School of Civil Engineering, Islamic Azad University, Tehran, Iran


Predicting the earthquake magnitude is a complex problem, which has been carried out in recent years. The machine learning, geophysical, and regression methods were used to predict earthquake magnitude in literature. Iran is located in a highly seismically active area; thus, earthquake prediction is considered as a great demand there. In this study, two time series algorithms are utilized to predict the magnitude of the earthquake based on previous earthquakes. These models are autoregressive conditional heteroscedasticity (GARCH), autoregressive integrated moving average (ARIMA), and the combination of ARIMA and GARCH by multiple linear regression (MLR) technique (model 3). The 9017 events are used to train and predict earthquake magnitude. On the other hand, 6188 events are applied for training models, and then 2829 events are utilized for testing it. The statistical parameters, such as correlation coefficient, root mean square error (RMSE), normalized square error (NMSE), and fractional bias, are calculated to evaluate the accuracy of each model. The results demonstrate that the ARIMA and model 3 can predict future earthquake magnitude better than other models.


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[1]        Aven, T., On how to define, understand and describe risk. Reliability Engineering & System Safety, 2010. 95(6): p. 623-631.
[2]        Florido, E., et al., Detecting precursory patterns to enhance earthquake prediction in Chile. Computers & Geosciences, 2015. 76: p. 112-120.
[3]        Špičák, A. and J. Vaněk, Earthquake swarms reveal submarine magma unrest induced by distant mega-earthquakes: Andaman Sea region. Journal of Asian Earth Sciences, 2016. 116: p. 155-163.
[4]        Verdugo, R. and J. González, Liquefaction-induced ground damages during the 2010 Chile earthquake. Soil Dynamics and Earthquake Engineering, 2015. 79: p. 280-295.
[5]        Keefer, D.K., Landslides caused by earthquakes. Geological Society of America Bulletin, 1984. 95(4): p. 406-421.
[6]        Cecioni, C., et al., Tsunami early warning system based on real-time measurements of hydro-acoustic waves. Procedia Engineering, 2014. 70: p. 311-320.
[7]        Fazendeiro Sá, L., A. Morales‐Esteban, and P. Durand Neyra, A seismic risk simulator for Iberia. Bulletin of the Seismological Society of America, 2016. 106(3): p. 1198-1209.
[8]        Tsai, C.-W., et al., Big data analytics: a survey. Journal of Big data, 2015. 2(1): p. 21.
[9]        Rouet‐Leduc, B., et al., Machine learning predicts laboratory earthquakes. Geophysical Research Letters, 2017. 44(18): p. 9276-9282.
[10]      Asencio–Cortés, G., et al., Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Computers & Geosciences, 2018. 115: p. 198-210.
[11]      Wang, Q., D.D. Jackson, and Y.Y. Kagan, California earthquakes, 1800–2007: A unified catalog with moment magnitudes, uncertainties, and focal mechanisms. Seismological Research Letters, 2009. 80(3): p. 446-457.
[12]      Naderpour, H., A.H. Rafiean, and P. Fakharian, Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering, 2018. 16: p. 213-219.
[13]      Shishegaran, A., M.R. Ghasemi, and H. Varaee, Performance of a novel bent-up bars system not interacting with concrete. Frontiers of Structural and Civil Engineering, 2019. 13(6): p. 1301-1315.
[14]      Shishegaran, A., et al., Developing conductive concrete containing wire rope and steel powder wastes for route deicing. Construction and Building Materials, 2020. 232: p. 117184.
[15]      Mohammadkhani, M.R., A. Shishegaran, and B. Shokrollahi, Forecasting probable maximum precipitation using innovative algorithm to estimate atmosphere precipitable water vapor. Mathematical Models in Engineering, 2019. 5(3): p. 90-96.
[16]      Shishegaran, A., A. Amiri, and M. Jafari, Seismic performance of box-plate, box-plate with UNP, box-plate with L-plate and ordinary rigid beam-to-column moment connections. Journal of Vibroengineering, 2018. 20(3): p. 1470-1487.
[17]      Shishegaran, A., S. Rahimi, and H. Darabi, Introducing box-plate beam-to-column moment connections. Vibroengineering PROCEDIA, 2017. 11: p. 200-204.
[18]      Reza Ghasemi, M. and A. Shishegaran, Role of slanted reinforcement on bending capacity SS beams. Vibroengineering PROCEDIA, 2017. 11: p. 195-199.
[19]      Naderpour, H. and P. Fakharian, A synthesis of peak picking method and wavelet packet transform for structural modal identification. KSCE Journal of Civil Engineering, 2016. 20(7): p. 2859-2867.
[20]      Naderpour, H., et al., Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Composite Structures, 2019. 215: p. 69-84.
[21]      Naderpour, H. and P. Fakharian, Predicting the torsional strength of reinforced concrete beams strengthened with FRP sheets in terms of artificial neural networks. Journal of Structural and Construction Engineering, doi, 2017. 10.
[22]      Shishegaran, A., et al., Computational predictions for estimating the maximum deflection of reinforced concrete panels subjected to the blast load. International Journal of Impact Engineering, 2020: p. 103527.
[23]      Box, G.E., et al., Time series analysis: forecasting and control. 2015: John Wiley & Sons.
[24]      Matei, M., Assessing volatility forecasting models: why GARCH models take the lead. Romanian Journal of Economic Forecasting, 2009. 12(4): p. 42-65.
[25]      Bollerslev, T., Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 1986. 31(3): p. 307-327.
[26]      Bates, J.M. and C.W. Granger, The combination of forecasts. Journal of the Operational Research Society, 1969. 20(4): p. 451-468.
[27]      Chang, J.C. and S.R. Hanna, Air quality model performance evaluation. Meteorology and Atmospheric Physics, 2004. 87(1-3): p. 167-196.