Hybrid CatBoost and SVR Model for Earthquake Prediction Using the LANL Earthquake Dataset
Abstract
Earthquakes have the potential to cause catastrophic structural and economic damage. This research explores the application of machine learning for earthquake prediction using LANL (Los Alamos National Laboratory) dataset. The data, obtained from a laboratory stick-slip friction experiment, simulate real earthquakes through digitized acoustic signals recorded against the time to failure of a granular layer. We introduced a hybrid model combining CatBoost and Support Vector Regression (SVR) to predict the time of the next earthquake, evaluating its performance against individual CatBoost and SVR models. The hybrid model demonstrated superior accuracy with a Mean Absolute Error (MAE) of 0.0825, outperforming the individual models. We implemented feature engineering to optimize the predictive capability of the models. Additionally, we compared our hybrid model's performance with previous studies to validate its efficacy. Our findings underscore the potential of machine learning, particularly hybrid models, in enhancing earthquake prediction accuracy. This study highlights the robustness and effectiveness of the hybrid CatBoost-SVR model, paving the way for advanced AI algorithms in seismology and contributing to improved disaster preparedness and mitigation strategies.
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Yuan, R., 2021. An improved K-means clustering algorithm for global earthquake catalogs and earthquake magnitude prediction. Journal of Seismology, 25(3), pp.1005-1020.
Li, J., Zhang, C., Chen, X., Cao, Y. and Jia, R., 2020, July. Improving abstractive summarization with iterative representation. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Chelidze, T., Melikadze, G., Kiria, T., Jimsheladze, T. and Kobzev, G., 2020. Statistical and non-linear dynamics methods of earthquake forecast: Application in the Caucasus. Frontiers in Earth Science, 8, p.194.
Al Banna, M.H., Taher, K.A., Kaiser, M.S., Mahmud, M., Rahman, M.S., Hosen, A.S. and Cho, G.H., 2020. Application of artificial intelligence in predicting earthquakes: state-of-the-art and future challenges. IEEE Access, 8, pp.192880-192923.
Nath, P., Saha, P., Middya, A.I. and Roy, S., 2021. Long-term time-series pollution forecast using statistical and deep learning methods. Neural Computing and Applications, pp.1-20.
Ludwin, R.: Earthquake prediction. https://pnsn.org/outreach/faq/earthquakeprediction (08 2019)
E. I. Alves, “Earthquake forecasting using neural networks: Results and future work,” Nonlinear Dynamics, vol. 44, no. 1, pp. 341–349, 2006.
A. Alexandridis, E. Chondrodima, E. Efthimiou, G. Papadakis, F. Vallianatos, and D. Triantis, “Large earthquake occurrence estimation based on radial basis function neural networks,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 9, pp. 5443–5453, 2014.
Kumar, N.; Kumar, P.; Chauhan, V.; Hazarika, D. Variable anelastic attenuation and site effect in estimating source parameters of various major earthquakes including Mw 7.8 Nepal and Mw 7.5 Hindu kush earthquake by using far-field strong-motion data. Int. J. Earth Sci. 2017, 106, 2371–2386.
Riguzzi, F.; Tan, H.; Shen, C. Surface volume and gravity changes due to significant earthquakes occurred in central Italy from 2009 to 2016. Int. J. Earth Sci. 2019, 108, 2047–2056.
B. Rouet-Leduc, C. Hulbert, N. Lubbers, K. Barros, C. J. Humphreys, and P. A. Johnson, “Machine learning predicts laboratory earthquakes,” Geophysical Research Letters, vol. 44, no. 18, pp. 9276–9282, 2017.
Rouet-Leduc, B.; Hulbert, C.; Lubbers, N.; Barros, K.; Humphreys, C.J.; Johnson, P.A. Machine learning predicts laboratory earthquakes. Geophys. Res. Lett. 2017, 44, 9276–9282.
Bolton, D.C.; Shokouhi, P.; Rouet-Leduc, B.; Hulbert, C.; Rivière, J.; Marone, C.; Johnson, P.A. Characterizing Acoustic Signals and Searching for Precursors during the Laboratory Seismic Cycle Using Unsupervised Machine Learning. Seism. Res. Lett. 2019, 90, 1088–1098.
Corbi, F.; Sandri, L.; Bedford, J.; Funiciello, F.; Brizzi, S.; Rosenau, M.; Lallemand, S. Machine learning can predict the timing and size of analog earthquakes. Geophys. Res. Lett. 2019, 46, 1303–1311.
Calabrese, L., Campanella, G. and Proverbio, E., 2013. Identification of corrosion mechanisms by univariate and multivariate statistical analysis during long term acoustic emission monitoring on a pre-stressed concrete beam. Corrosion Science, 73, pp.161-171.
Diakhate, M., Bastidas-Arteaga, E., Pitti, R.M. and Schoefs, F., 2017. Cluster analysis of acoustic emission activity within wood material: Towards a real-time monitoring of crack tip propagation. Engineering Fracture Mechanics, 180, pp.254-267.
Li, L., Lomov, S. V., & Yan, X. (2015). Correlation of acoustic emission with optically observed damage in a glass/epoxy woven laminate under tensile loading. Composite Structures, 123, 45–53.
Fallahi, N., Nardoni, G., Palazzetti, R., & Zucchelli, A. (2016). Pattern recognition of acoustic emission signal during the mode I fracture mechanisms in carbon-epoxy composite. In 32nd European conference on acoustic emission testing 2016 (pp. 415–421). https://doi.org/10.5937/fmet1604415f
Louis, S.-Y. M., Nasiri, A., Bao, J., Cui, Y., Zhao, Y., Jin, J., & Hu, J. (2020). Remaining useful strength (RUS) prediction of SICF-SICM composite materials using deep learning and acoustic emission. Applied Sciences, 10(8). https://doi.org/10.3390/app10082680
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017). Long short-term memory network for remaining useful life estimation. 2017 IEEE international conference on prognostics and health management (ICPHM) (pp. 88–95). https://doi.org/10.1109/ICPHM.2017.7998311
Asencio–Cortés, G., Morales–Esteban, A., Shang, X. and Martínez–Álvarez, F., 2018. Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Computers & Geosciences, 115, pp.198-210.
Panakkat, A. and Adeli, H., 2007. Neural network models for earthquake magnitude prediction using multiple seismicity indicators. International journal of neural systems, 17(01), pp.13-33.
Otari, G.V. and Kulkarni, R.V., 2012. A review of application of data mining in earthquake prediction. International Journal of Computer Science and Information Technologies, 3(2), pp.3570-3574.
Kavianpour, P., Kavianpour, M., Jahani, E. and Ramezani, A., 2023. A CNN-BiLSTM model with attention mechanism for earthquake prediction. The Journal of Supercomputing, 79(17), pp.19194-19226.
Mousavi, S.M. and Beroza, G.C., 2019. Bayesian-deep-learning estimation of earthquake location from single-station observations. arXiv preprint arXiv:1912.01144.
Ristea, N.C. and Radoi, A., 2021. Complex neural networks for estimating epicentral distance, depth, and magnitude of seismic waves. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5.
Johnson, P.A., Rouet-Leduc, B., Pyrak-Nolte, L.J., Beroza, G.C., Marone, C.J., Hulbert, C., Howard, A., Singer, P., Gordeev, D., Karaflos, D. and Levinson, C.J., 2021. Laboratory earthquake forecasting: A machine learning competition. Proceedings of the national academy of sciences, 118(5), p.e2011362118.
Mousavi, S.M., Ellsworth, W.L., Zhu, W., Chuang, L.Y. and Beroza, G.C., 2020. Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature communications, 11(1), p.3952.
Zhu, L., Peng, Z., McClellan, J., Li, C., Yao, D., Li, Z. and Fang, L., 2019. Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7. 9 Wenchuan Earthquake. Physics of the Earth and Planetary Interiors, 293, p.106261.
Mousavi, S.M. and Beroza, G.C., 2019. Bayesian-deep-learning estimation of earthquake location from single-station observations. arXiv preprint arXiv:1912.01144.
Ren, C.X., Hulbert, C., Johnson, P.A. and Rouet-Leduc, B., 2020. Machine learning and fault rupture: a review. Advances in Geophysics, 61, pp.57-107.
Zhao, Y. and Gorse, D., 2024. Earthquake prediction from seismic indicators using tree-based ensemble learning. Natural Hazards, pp.1-27.
Rouet‐Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C.J. and Johnson, P.A., 2017. Machine learning predicts laboratory earthquakes. Geophysical Research Letters, 44(18), pp.9276-9282.
B. Rouet-Leduc, C. Hulbert, N. Lubbers, K. Barros, C. J. Humphreys, and P. A. Johnson, “Machine learning predicts laboratory earthquakes,” Geophysical Research Letters, vol. 44, no. 18, pp. 9276–9282, 2017.
Karimpouli, S., Caus, D., Grover, H., Martínez-Garzón, P., Bohnhoff, M., Beroza, G.C., Dresen, G., Goebel, T., Weigel, T. and Kwiatek, G., 2023. Explainable machine learning for labquake prediction using catalog-driven features. Earth and Planetary Science Letters, 622, p.118383.
Karimpouli, S., Kwiatek, G., Martínez-Garzón, P., Dresen, G. and Bohnhoff, M., 2024. Event-based features: An improved feature extraction approach to enrich machine learning based labquake forecasting (No. EGU24-5044). Copernicus Meetings.
Affinito, R., Wood, C., Derek, E. and Marone, C., 2023. The Stability Transition from Stable to Unstable Frictional Slip with Finite Pore Pressure. Authorea Preprints.
Dhotre, S., Doshi, K., Satish, S. and Wagaskar, K., 2022, June. Exploring quantum machine learning (qml) for earthquake prediction. In 2022 2nd International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE.
Ridzwan, N.S.M. and Yusoff, S.H.M., 2023. Machine learning for earthquake prediction: A review (2017–2021). Earth Science Informatics, 16(2), pp.1133-1149.
Zhu, W., Wu, M., Xie, Q. and Chen, Y., 2023. Post-earthquake rapid assessment method for electrical function of equipment in substations. IEEE Transactions on Power Delivery.
Li, Y., Jia, C., Chen, H., Su, H., Chen, J. and Wang, D., 2023. Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features. Sustainability, 15(18), p.13847.
Gautam, D., Bhattarai, A. and Rupakhety, R., 2024. Machine learning and soft voting ensemble classification for earthquake induced damage to bridges. Engineering Structures, 303, p.117534.
Li, Z., Lei, H., Ma, E., Lai, J. and Qiu, J., 2023. Ensemble technique to predict post-earthquake damage of buildings integrating tree-based models and tabular neural networks. Computers & Structures, 287, p.107114.
Ocak, A., Işıkdağ, Ü., Bekdaş, G., Nigdeli, S.M., Kim, S. and Geem, Z.W., 2024. Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning. CMES-Computer Modeling in Engineering & Sciences, 138(3).
Bergen, K.J., Chen, T. and Li, Z., 2019. Preface to the focus section on machine learning in seismology. Seismological Research Letters , 90(2A), pp.477-480.
Karimpouli, S., Kwiatek, G., Martínez-Garzón, P., Dresen, G. and Bohnhoff, M., 2024. Event-based features: An improved feature extraction approach to enrich machine learning based labquake forecasting (No. EGU24-5044). Copernicus Meetings.
Brykov, M.N., Petryshynets, I., Pruncu, C.I., Efremenko, V.G., Pimenov, D.Y., Giasin, K., Sylenko, S.A. and Wojciechowski, S., 2020. Machine learning modelling and feature engineering in seismology experiment. Sensors, 20(15), p.4228.
Jasperson, H., Bolton, D.C., Johnson, P., Guyer, R., Marone, C. and de Hoop, M.V., 2021. Attention network forecasts time‐to‐failure in laboratory shear experiments. Journal of Geophysical Research: Solid Earth, 126(11), p.e2021JB022195.
DOI: https://doi.org/10.31449/inf.v49i14.6524
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