| [1] | Abrahamson, N. A., Silva, W. J., Kamai, R., 2014. Summary of the ASK14 Ground Motion Relation for Active Crustal Regions. Earthquake Spectra, 30(3): 1025–1055. https://doi.org/10.1193/070913eqs198m |
| [2] | Abrahamson, N., Silva, W., 2008. Summary of the Abrahamson & Silva NGA Ground-Motion Relations. Earthquake Spectra, 24(1): 67–97. https://doi.org/10.1193/1.2924360 |
| [3] | Akkar, S., Sandıkkaya, M. A., Bommer, J. J., 2014. Empirical Ground-Motion Models for Point- and Extended-Source Crustal Earthquake Scenarios in Europe and the Middle East. Bulletin of Earthquake Engineering, 12(1): 359–387. https://doi.org/10.1007/s10518-013-9461-4 |
| [4] | Alavi, A. H., Gandomi, A. H., 2011. Prediction of Principal Ground-Motion Parameters Using a Hybrid Method Coupling Artificial Neural Networks and Simulated Annealing. Computers & Structures, 89(23/24): 2176–2194. https://doi.org/10.1016/j.compstruc.2011.08.019 |
| [5] | Alavi, A. H., Gandomi, A. H., Modaresnezhad, M., et al., 2011. New Ground-Motion Prediction Equations Using Multi Expression Programing. Journal of Earthquake Engineering, 15(4): 511–536. https://doi.org/10.1080/13632469.2010.526752 |
| [6] | Ambraseys, N. N., Douglas, J., 2003. Near-Field Horizontal and Vertical Earthquake Ground Motions. Soil Dynamics and Earthquake Engineering, 23(1): 1–18. https://doi.org/10.1016/S0267-7261(02)00153-7 |
| [7] | Ambraseys, N. N., Simpson, K. A., Bommer, J. J., 1996. Prediction of Horizontal Response Spectra in Europe. Earthquake Engineering & Structural Dynamics, 25(4): 371–400. https://doi.org/10.1002/(sici)1096-9845(199604)25:4371:aid-eqe550>3.0.co;2-a doi: 10.1002/(sici)1096-9845(199604)25:4371:aid-eqe550>3.0.co;2-a |
| [8] | Aptikaev, F., Kopnichev, J., 1980. Correlation between Seismic Vibration Parameters and Type of Faulting. In: Proceedings of Seventh World Conference on Earthquake Engineering. September 8–13, 1980, Istanbul |
| [9] | Boore, D. M., Stewart, J. P., Seyhan, E., et al., 2013. NGA-West2 Equations for Predicting Response Spectral Accelerations for Shallow Crustal Earthquakes. In: PEER Report No. 2013. Pacific Earthquake Engineering Research Center, University of California, Berkeley |
| [10] | Bozorgnia, Y., Abrahamson, N. A., Atik, L. A., et al., 2014. NGA-West2 Research Project. Earthquake Spectra, 131(3): 409–444 https://www.researchgate.net/publication/261359931_NGA-West2_research_project |
| [11] | Breiman, L., Friedman, J. H., Olshen, R. A., et al., 1984. Classification and Regression Trees (CART). Biometrics, 40(3): 874. https://doi.org/10.2307/2530946 |
| [12] | Campbell, K. W., 1985. Strong Motion Attenuation Relations: A Ten-Year Perspective. Earthquake Spectra, 1(4): 759–804. https://doi.org/10.1193/1.1585292 |
| [13] | Campbell, K. W., Bozorgnia, Y., 2008. NGA Ground Motion Model for the Geometric Mean Horizontal Component of PGA, PGV, PGD and 5% Damped Linear Elastic Response Spectra for Periods Ranging from 0.01 to 10 S. Earthquake Spectra, 24(1): 139–171. https://doi.org/10.1193/1.2857546 |
| [14] | Campbell, K. W., Bozorgnia, Y., 2014. NGA-West2 Ground Motion Model for the Average Horizontal Components of PGA, PGV, and 5% Damped Linear Acceleration Response Spectra. Earthquake Spectra, 30(3): 1087–1115. https://doi.org/10.1193/062913eqs175m |
| [15] | Cheng, H. L., Zhou, J. M., Chen, Z. Y., et al., 2021. A Comparative Study of the Seismic Performances and Failure Mechanisms of Slopes Using Dynamic Centrifuge Modeling. Journal of Earth Science, 32(5): 1166–1173. https://doi.org/10.1007/s12583-021-1481-4 |
| [16] | Chiou, B. S.-J., Youngs, R. R., 2014. Update of the Chiou and Youngs NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra. Earthquake Spectra, 30(3): 1117–1153. https://doi.org/10.1193/072813eqs219m |
| [17] | Derakhshani, A., Foruzan, A. H., 2019. Predicting the Principal Strong Ground Motion Parameters: A Deep Learning Approach. Applied Soft Computing, 80: 192–201. https://doi.org/10.1016/j.asoc.2019.03.029 |
| [18] | Derras, B., Bard, P. Y., Cotton, F., 2014. Towards Fully Data Driven Ground-Motion Prediction Models for Europe. Bulletin of Earthquake Engineering, 12(1): 495–516. https://doi.org/10.1007/s10518-013-9481-0 |
| [19] | Derras, B., Bard, P. Y., Cotton, F., et al., 2012. Adapting the Neural Network Approach to PGA Prediction: An Example Based on the KIK-NET Data. Bulletin of the Seismological Society of America, 102(4): 1446–1461. https://doi.org/10.1785/0120110088 |
| [20] | Douglas, J., 2003. Earthquake Ground Motion Estimation Using Strong-Motion Records: A Review of Equations for the Estimation of Peak Ground Acceleration and Response Spectral Ordinates. Earth-Science Reviews, 61(1/2): 43–104. https://doi.org/10.1016/S0012-8252(02)00112-5 |
| [21] | Friedman, J. H., 2001. Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5): 1189–1232. https://doi.org/10.1214/aos/1013203451 |
| [22] | Gandomi, A. H., Alavi, A. H., Mousavi, M., et al., 2011. A Hybrid Computational Approach to Derive New Ground-Motion Prediction Equations. Engineering Applications of Artificial Intelligence, 24(4): 717–732. https://doi.org/10.1016/j.engappai.2011.01.005 |
| [23] | Hao, H. Z., Gu, Q., Hu, X. M., 2021. Research Advances and Prospective in Mineral Intelligent Identification Based on Machine Learning. Earth Science, 46(9): 3091–3106. https://doi.org/10.3799/dqkx.2020.360 |
| [24] | Heath, D. C., Wald, D. J., Worden, C. B., et al., 2020. A Global Hybrid VS30 Map with a Topographic Slope-Based Default and Regional Map Insets. Earthquake Spectra, 36(3): 1570–1584. https://doi.org/10.1177/8755293020911137 |
| [25] | Idriss, I. M., 2013. NGA-West2 Model for Estimating Average Horizontal Values of Pseudo-Absolute Spectral Accelerations Generated by Crustal Earthquakes. In: PEER Report No. 2013. Pacific Earthquake Engineering Research Center, University of California, Berkeley |
| [26] | Jafariavval, Y., Derakhshani, A., 2020. New Formulae for Capacity Energy-Based Assessment of Liquefaction Triggering. Marine Georesources & Geotechnology, 38(2): 214–222. https://doi.org/10.1080/1064119x.2019.1566297 |
| [27] | Kafaei Mohammadnejad, A., Mousavi, S. M., Torabi, M., et al., 2012. Robust Attenuation Relations for Peak Time-Domain Parameters of Strong Ground Motions. Environmental Earth Sciences, 67(1): 53–70. https://doi.org/10.1007/s12665-011-1479-9 |
| [28] | Kayabali, K., Beyaz, T., 2011. Strong Motion Attenuation Relationship for Turkey—A Different Perspective. Bulletin of Engineering Geology and the Environment, 70(3): 467–481. https://doi.org/10.1007/s10064-010-0335-6 |
| [29] | Ke, G. L., Meng, Q., Finley, T., et al., 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: NIPS'17: Proceeding of the 31st International Conference on Neural Information Processing Systems, December 2017, New York |
| [30] | Molnar, C., 2019. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Leanpub |
| [31] | Nagelkerke, N. J. D., 1991. A Note on a General Definition of the Coefficient of Determination. Biometrika, 78(3): 691–692. https://doi.org/10.1093/biomet/78.3.691 |
| [32] | Şen, Z., 2011. Supervised Fuzzy Logic Modeling for Building Earthquake Hazard Assessment. Expert Systems with Applications, 38(12): 14564–14573. https://doi.org/10.1016/j.eswa.2011.05.026 |
| [33] | 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): 1–10. https://doi.org/10.1007/s12517-020-5211-5 |
| [34] | Singer, D. A., 2021. How Deep Learning Networks could be Designed to Locate Mineral Deposits. Journal of Earth Science, 32(2): 288–292. https://doi.org/10.1007/s12583-020-1399-2 |
| [35] | Thomas, S., Pillai, G. N., Pal, K., 2017. Prediction of Peak Ground Acceleration Using ϵ-SVR, Ν-SVR and Ls-SVR Algorithm. Geomatics, Natural Hazards and Risk, 8(2): 177–193. https://doi.org/10.1080/19475705.2016.1176604 |
| [36] | Tobler, W. R., 1970. A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46(Sup1): 234–240. https://doi.org/10.2307/143141 |
| [37] | Tuv, E., Borisov, A., Runger, G., et al., 2009. Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination. Journal of Machine Learning Research, 10(3): 1341–1366. https://doi.org/10.1145/1577069.1755828 |
| [38] | Wen, R. Z., Xu, P. B., Ren, Y. F., et al., 2017. Development of the Strong-Motion Flatfile. Earthquake Engineering and Engineering Dynamics, 37(3): 38–47. https://doi.org/10.13197/j.eeev.2017.03.38.wenrz.004 (in Chinese with English Abstract) |
| [39] | Worden, C. B., Wald, D. J., Allen, T. I., et al., 2010. A Revised Ground-Motion and Intensity Interpolation Scheme for ShakeMap. Bulletin of the Seismological Society of America, 100(6): 3083–3096. https://doi.org/10.1785/0120100101 |
| [40] | Yenier, E., Erdoğan, Ö., Akkar, S., 2008. Empirical Relationships for Magnitude and Source-to-Site Distance Conversions Using Recently Compiled Turkish Strong-Ground Motion Database. In: The 14th World Conference on Earthquake Engineering. October 12–17, 2008, Beijing |
| [41] | Youngs, R. R., Chiou, B. S.-J., Silva, W. J., et al., 1997. Strong Ground Motion Attenuation Relationships for Subduction Zone Earthquakes. Seismological Research Letters, 68(1): 58–73. https://doi.org/10.1785/gssrl.68.1.58 |
| [42] | Youngs, R. R., Day, S. M., Stevens, J. L., 1988. Near Field Ground Motions on Rock for Large Subduction Earthquakes. In: Thun, J. L. V., ed., Earthquake Engineering and Soil Dynamics Ⅱ: Recent Advances in Ground-Motion Evaluation. American Society of Civil Engineers, Reston. 445–462 |
| [43] | Zuo, R. G., Peng, Y., Li, T., et al., 2021. Challenges of Geological Prospecting Big Data Mining and Integration Using Deep Learning Algorithms. Earth Science, 46(1): 350–358. https://doi.org/10.3799/dqkx.2020.111 |