[1] Aven T. On how to define, understand and describe risk. Reliab Eng Syst Saf 2010;95:623–31. doi:10.1016/j.ress.2010.01.011.
[2] Florido E, Martínez-Álvarez F, Morales-Esteban A, Reyes J, Aznarte-Mellado JL. Detecting precursory patterns to enhance earthquake prediction in Chile. Comput Geosci 2015;76:112–20. doi:10.1016/j.cageo.2014.12.002.
[3] Špičák A, Vaněk J. Earthquake swarms reveal submarine magma unrest induced by distant mega-earthquakes: Andaman Sea region. J Asian Earth Sci 2016;116:155–63. doi:10.1016/j.jseaes.2015.11.017.
[4] Verdugo R, González J. Liquefaction-induced ground damages during the 2010 Chile earthquake. Soil Dyn Earthq Eng 2015;79:280–95. doi:10.1016/j.soildyn.2015.04.016.
[5] Keefer DK. Landslides caused by earthquakes. Geol Soc Am Bull 1984;95:406–21.
[6] Cecioni C, Bellotti G, Romano A, Abdolali A, Sammarco P, Franco L. Tsunami Early Warning System based on Real-time Measurements of Hydro-acoustic Waves. Procedia Eng 2014;70:311–20. doi:10.1016/j.proeng.2014.02.035.
[7] Fazendeiro Sá L, Morales‐Esteban A, Durand Neyra P. A Seismic Risk Simulator for Iberia. Bull Seismol Soc Am 2016;106:1198–209. doi:10.1785/0120150195.
[8] Tsai C-W, Lai C-F, Chao H-C, Vasilakos A V. Big data analytics: a survey. J Big Data 2015;2:21. doi:10.1186/s40537-015-0030-3.
[9] Rouet-Leduc B, Hulbert C, Lubbers N, Barros K, Humphreys CJ, Johnson PA. Machine Learning Predicts Laboratory Earthquakes. Geophys Res Lett 2017;44:9276–82. doi:10.1002/2017GL074677.
[10] Asencio–Cortés G, Morales–Esteban A, Shang X, Martínez–Álvarez F. Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Comput Geosci 2018;115:198–210. doi:10.1016/j.cageo.2017.10.011.
[11] Wang Q, Jackson DD, Kagan YY. California Earthquakes, 1800-2007: A Unified Catalog with Moment Magnitudes, Uncertainties, and Focal Mechanisms. Seismol Res Lett 2009;80:446–57. doi:10.1785/gssrl.80.3.446.
[12] Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16:213–9. doi:10.1016/j.jobe.2018.01.007.
[13] Shishegaran A, Ghasemi MR, Varaee H. Performance of a novel bent-up bars system not interacting with concrete. Front Struct Civ Eng 2019;13:1301–15. doi:10.1007/s11709-019-0552-4.
[14] Shishegaran A, Khalili MR, Karami B, Rabczuk T, Shishegaran A. Computational predictions for estimating the maximum deflection of reinforced concrete panels subjected to the blast load. Int J Impact Eng 2020;139:103527. doi:10.1016/j.ijimpeng.2020.103527.
[15] Shishegaran A, Daneshpajoh F, Taghavizade H, Mirvalad S. Developing conductive concrete containing wire rope and steel powder wastes for route deicing. Constr Build Mater 2020;232:117184. doi:10.1016/j.conbuildmat.2019.117184.
[16] Mohammadkhani MR, Shishegaran A, Shokrollahi B. Forecasting probable maximum precipitation using innovative algorithm to estimate atmosphere precipitable water vapor. Math Model Eng 2019;5:90–6. doi:10.21595/mme.2019.20935.
[17] Shishegaran A, Amiri A, Jafari MA. Seismic performance of box-plate, box-plate with UNP, box-plate with L-plate and ordinary rigid beam-to-column moment connections. J Vibroengineering 2018;20:1470–87. doi:10.21595/jve.2017.18716.
[18] Shishegaran A, Rahimi S, Darabi H. Introducing box-plate beam-to-column moment connections. Vibroengineering PROCEDIA 2017;11:200–4. doi:10.21595/vp.2017.18548.
[19] Reza Ghasemi M, Shishegaran A. Role of slanted reinforcement on bending capacity SS beams. Vibroengineering PROCEDIA 2017;11:195–9. doi:10.21595/vp.2017.18544.
[20] Naderpour H, Fakharian P. A synthesis of peak picking method and wavelet packet transform for structural modal identification. KSCE J Civ Eng 2016;20:2859–67. doi:10.1007/s12205-016-0523-4.
[21] Naderpour H, Nagai K, Fakharian P, Haji M. Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Compos Struct 2019;215:69–84. doi:10.1016/j.compstruct.2019.02.048.
[22] Naderpour H, Fakharian P. Predicting the torsional strength of reinforced concrete beams strengthened with FRP sheets in terms of artificial neural networks. J Struct Constr Eng 2018;5:20–35. doi:10.22065/JSCE.2017.70668.1023.
[23] Box GEP, Jenkins GM, Reinsel GC, Ljung GM. Time series analysis: forecasting and control. John Wiley & Sons; 2015.
[24] Matei M. Assessing volatility forecasting models: why GARCH models take the lead. Rom J Econ Forecast 2009;12:42–65.
[25] Bollerslev T. Generalized autoregressive conditional heteroskedasticity. J Econom 1986;31:307–27.
[26] Bates JM, Granger CWJ. The Combination of Forecasts. J Oper Res Soc 1969;20:451–68. doi:10.1057/jors.1969.103.
[27] Chang JC, Hanna SR. Air quality model performance evaluation. Meteorol Atmos Phys 2004;87. doi:10.1007/s00703-003-0070-7.