Forecasting the Magnitude Category Based on The Flores Sea Earthquake
Abstract
Earthquakes are a phenomenon that is still a mystery in terms of predicting events, one of which is the magnitude. As technology develops, there are many algorithms that can be used as approaches in earthquake forecasting. In the context of magnitude forecasting, the application of GaussianNB, Random Forest, and SVM has the potential to reveal these patterns and relationships in the data. With the six main phases of this research, namely data acquisition, data pre-processing, feature selection, model training, forecast result evaluation, and performance analysis, this study is expected to contribute to the development of more accurate and effective earthquake forecasting methods. From these results we first obtain the result that the GaussianNB model has a relatively simple and fast method in training its model. However, the weakness lies in the assumption of a Gaussian distribution, which may not always suit the complex and diverse characteristics of earthquake data. Second, Random Forest, this method can increase accuracy and overcome the overfitting problem that occurs when forecasting magnitudes. In contrast to GaussianNB, it tends to result in models with greater complexity and requires more time to compute. The third option is SVM, which has both benefits and drawbacks that must be taken into account. The capacity of SVM to separate data that has both linear and nonlinear separation is one of its key advantages; nevertheless, the main drawback is that it is sensitive to hyperparameter adjustments.
Downloads
References
A. Khusnani, A. Jufriansah, and M. Afriyanto, “Utilization of Seismic Data as a Tsunami Vulnerability Review,” Indonesian Review of Physics, vol. 5, no. 2, pp. 66–72, 2022, doi: https://doi.org/10.12928/irip.v5i2.6706.
A. Jufriansah et al., “Estimation of Flores Sea Aftershock Rupture Data Based on AI,” Indones. Rev. Phys, vol. 6, no. 1, pp. 46–56, 2023, doi: 10.12928/irip.v6i1.6705.
R. Maneno, B. J. Sentosa, and G. Rachman, “Relocation Of Earthquake Hypocenter In The Flores Region Using Hypo71,” IPTEK The Journal of Engineering, vol. 5, no. 2, May 2019, doi: 10.12962/j23378557.v5i2.a5024.
R. Rahardiawan and C. Purwanto, “Struktur Geologi Laut Flores, Nusa Tenggara Timur.,” Jurnal Geologi Kelautan, vol. 12, no. 3, pp. 153–163, 2014, doi: DOI: 10.32693/jgk.12.3.2014.256.
L. Handayani, “Seismic Hazard Analysis of Maumere, Flores: a Review of the Earthquake Sources,” in Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 2020, pp. 1–7. doi: 10.4108/eai.12-10-2019.2296247.
E. D. Banggut and B. J. Santosa, “Estimation of the Source Parameters of the Flores Earthquake and Their Correlation to Aftershocks,” IPTEK The Journal of Engineering, vol. 5, no. 1, pp. 14–17, 2019, doi: http://dx.doi.org/10.12962/j23378557.v5i1.a5021.
A. Jufriansah, A. Khusnani, Y. Pramudya, and M. Afriyanto, “Comparison of aftershock behavior of the flores sea 12 december 1992 and 14 december 2021,” Journal of Physics: Theories and Applications, vol. 7, no. 1, pp. 65-74., 2023, doi: https://doi.org/10.20961/jphystheor-appl.v7i1.71609.
A. S. N. Alarifi, N. S. N. Alarifi, and S. Al-Humidan, “Earthquakes magnitude predication using artificial neural network in northern Red Sea area,” J King Saud Univ Sci, vol. 24, no. 4, pp. 301–313, Oct. 2012, doi: 10.1016/j.jksus.2011.05.002.
S. Cihad, E. Al-Heety, and W. Abdulnaby, “Relocation and Magnitude for Earthquakes in the Outer Arabian Platform of Iraq,” Iraqi Geological Journal, vol. 56, no. 1B, pp. 198–215, Feb. 2023, doi: 10.46717/igj.56.1B.15ms-2023-2-23.
M. N. Shodiq, D. H. Kusuma, M. G. Rifqi, A. R. Barakbah, and T. Harsono, “Spatial analisys of magnitude distribution for earthquake prediction using neural network based on automatic clustering in Indonesia,” in 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), IEEE, Sep. 2017, pp. 246–251. doi: 10.1109/KCIC.2017.8228594.
W.-Y. Kim, L. Ottemöller, and P. G. Richards, “A Regional Sn Magnitude Scale mb(Sn) and Estimates of Moment Magnitude for Earthquakes along the Northern Mid-Atlantic Ridge,” Bulletin of the Seismological Society of America, vol. 110, no. 6, pp. 3158–3173, Dec. 2020, doi: 10.1785/0120190304.
D. Kusumawati et al., “Sensitivity analysis of moment magnitude (Mw) spectral fitting parameters based on orthogonal design: using aftershocks of the 2019 Mw 6.5 Ambon, Indonesia earthquake,” Bulletin of Earthquake Engineering, vol. 21, no. 4, pp. 1793–1819, Mar. 2023, doi: 10.1007/s10518-022-01606-6.
A. Jufriansah, Y. Pramudya, A. Khusnani, and S. Saputra, “Analysis of Earthquake Activity in Indonesia by Clustering Method,” Journal of Physics: Theories and Applications, vol. 5, no. 2, p. 92, Sep. 2021, doi: 10.20961/jphystheor-appl.v5i2.59133.
F. Aden‐Antoniów, W. B. Frank, and L. Seydoux, “An Adaptable Random Forest Model for the Declustering of Earthquake Catalogs,” J Geophys Res Solid Earth, vol. 127, no. 2, Feb. 2022, doi: 10.1029/2021JB023254.
A. E. Ruano, G. Madureira, O. Barros, H. R. Khosravani, M. G. Ruano, and P. M. Ferreira, “Seismic detection using support vector machines,” Neurocomputing, vol. 135, pp. 273–283, Jul. 2014, doi: 10.1016/j.neucom.2013.12.020.
H. S. Kuyuk, E. Yildirim, E. Dogan, and G. Horasan, “Application of k-means and Gaussian mixture model for classification of seismic activities in Istanbul,” Nonlinear Process Geophys, vol. 19, no. 4, pp. 411–419, Aug. 2012, doi: 10.5194/npg-19-411-2012.
A. Malekloo, E. Ozer, M. AlHamaydeh, and M. Girolami, “Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights,” Struct Health Monit, vol. 21, no. 4, pp. 1906–1955, Jul. 2022, doi: 10.1177/14759217211036880.
F. M. Wani, J. Vemuri, C. Rajaram, and K. S. K. K. Reddy, “Investigating the efficiency of machine learning algorithms in classifying pulse-like ground motions,” J Seismol, Aug. 2023, doi: 10.1007/s10950-023-10168-2.
R. J. Geller, “Earthquake prediction: a critical review,” Geophys J Int, vol. 131, no. 3, pp. 425–450, Dec. 1997, doi: 10.1111/j.1365-246X.1997.tb06588.x.
A. P. Menon, A. Varghes, J. P. Joseph, J. Sajan, and N. Francis, “Performance Analysis of different Classifiers for Earthquake prediction: PACE,” International Journal of Innovative Research in Technology (IJIRT), vol. 7, no. 2, pp. 142–146, 2020, doi: https://doi.org/10.6084/m9.figshare.12623573.
S. Baranov, C. Narteau, and P. Shebalin, “Modeling and Prediction of Aftershock Activity,” Surv Geophys, Apr. 2022, doi: 10.1007/s10712-022-09698-0.
C. Jiang, X. Wei, X. Cui, and D. You, “Application of support vector machine to synthetic earthquake prediction,” Earthquake Science, vol. 22, no. 3, pp. 315–320, Jun. 2009, doi: 10.1007/s11589-009-0315-8.
M. N. Shodiq, D. H. Kusuma, M. G. Rifqi, A. R. Barakbah, and T. Harsono, “Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia,” JOIV : International Journal on Informatics Visualization, vol. 2, no. 1, p. 37, Feb. 2018, doi: 10.30630/joiv.2.1.106.
S. Marhain, A. N. Ahmed, M. A. Murti, P. Kumar, and A. El-Shafie, “Investigating the application of artificial intelligence for earthquake prediction in Terengganu,” Natural Hazards, vol. 108, no. 1, pp. 977–999, Aug. 2021, doi: 10.1007/s11069-021-04716-7.
K. Weiqi, W. Weisong, and Z. Maoxing, “Integrated Learning Algorithms with Bayesian Optimization for Mild Steel Mechanical Properties Prediction,” Knowledge-based Engineering and Sciences, vol. 3, no. 3, pp. 101–112, 2022, doi: 10.51526/kbes.2022.3.3.101-112.
W. Astuti, R. Akmeliawati, W. Sediono, and M. J. E. Salami, “Hybrid Technique Using Singular Value Decomposition (SVD) and Support Vector Machine (SVM) Approach for Earthquake Prediction,” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 7, no. 5, pp. 1719–1728, May 2014, doi: 10.1109/JSTARS.2014.2321972.
S. Karimzadeh, M. Matsuoka, J. Kuang, and L. Ge, “Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective,” ISPRS Int J Geoinf, vol. 8, no. 10, p. 462, Oct. 2019, doi: 10.3390/ijgi8100462.
Md. H. Al Banna et al., “Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges,” IEEE Access, vol. 8, pp. 192880–192923, 2020, doi: 10.1109/ACCESS.2020.3029859.
D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl Soft Comput, vol. 97, p. 105524, Dec. 2020, doi: 10.1016/j.asoc.2019.105524.
V. Tschannen, A. Ghanim, and N. Ettrich, “Partial automation of the seismic to well tie with deep learning and Bayesian optimization,” Comput Geosci, vol. 164, p. 105120, Jul. 2022, doi: 10.1016/j.cageo.2022.105120.
S. Panigrahi and H. S. Behera, “Effect of Normalization Techniques on Univariate Time Series Forecasting using Evolutionary Higher Order Neural Network,” 2013.
A. Aprianti, A. Jufriansah, B. Donuata, A. Khusnani, and J. Ayuba, “Comparison of K-Means Algorithm and DBSCAN on Aftershock Activity in the Flores Sea: Seismic,” Journal of Novel Engineering Science and Technology, vol. 2, no. 3, pp. 77–82, 2019, doi: 10.56741/jnest.v2i03.393.
B. B. Odevci, E. Emsen, and M. N. Aydin, “Machine learning algorithms for lamb survival,” Comput Electron Agric, vol. 182, p. 105995, Mar. 2021, doi: 10.1016/j.compag.2021.105995.
A. Yudhana, D. Sulistyo, and I. Mufandi, “GIS-based and Naïve Bayes for nitrogen soil mapping in Lendah, Indonesia,” Sens Biosensing Res, vol. 33, p. 100435, Aug. 2021, doi: 10.1016/j.sbsr.2021.100435.
M. S. Abdalzaher, S. S. R. Moustafa, M. Abd-Elnaby, and M. Elwekeil, “Comparative Performance Assessments of Machine-Learning Methods for Artificial Seismic Sources Discrimination,” IEEE Access, vol. 9, pp. 65524–65535, 2021, doi: 10.1109/ACCESS.2021.3076119.
M. S. Abdalzaher, M. S. Soliman, and S. M. El-Hady, “Seismic Intensity Estimation for Earthquake Early Warning Using Optimized Machine Learning Model,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–11, 2023, doi: 10.1109/TGRS.2023.3296520.
Y. Li, C. Yan, W. Liu, and M. Li, “A principle component analysis-based random forest with the potential nearest neighbor method for automobile insurance fraud identification,” Appl Soft Comput, vol. 70, pp. 1000–1009, Sep. 2018, doi: 10.1016/j.asoc.2017.07.027.
M. Belgiu and L. Drăguţ, “Random forest in remote sensing: A review of applications and future directions,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, pp. 24–31, Apr. 2016, doi: 10.1016/j.isprsjprs.2016.01.011.
A. Lulli, L. Oneto, and D. Anguita, “Mining Big Data with Random Forests,” Cognit Comput, vol. 11, no. 2, pp. 294–316, Apr. 2019, doi: 10.1007/s12559-018-9615-4.
F. Soleimani, “Analytical seismic performance and sensitivity evaluation of bridges based on random decision forest framework,” Structures, vol. 32, pp. 329–341, Aug. 2021, doi: 10.1016/j.istruc.2021.02.049.
Y. Ao, H. Li, L. Zhu, S. Ali, and Z. Yang, “Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm,” J Pet Sci Eng, vol. 173, pp. 781–792, Feb. 2019, doi: 10.1016/j.petrol.2018.10.048.
J. Roiz-Pagador, A. Chacon-Maldonado, R. Ruiz, and G. Asencio-Cortes, “Earthquake Prediction in California Using Feature Selection Techniques,” 2022, pp. 728–738. doi: 10.1007/978-3-030-87869-6_69.
R. Mallouhy, C. A. Jaoude, C. Guyeux, and A. Makhoul, “Major earthquake event prediction using various machine learning algorithms,” in 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), IEEE, Dec. 2019, pp. 1–7. doi: 10.1109/ICT-DM47966.2019.9032983.
I. M. Murwantara, P. Yugopuspito, and R. Hermawan, “Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 18, no. 3, p. 1331, Jun. 2020, doi: 10.12928/telkomnika.v18i3.14756.
D. Tao, Q. Ma, S. Li, Z. Xie, D. Lin, and S. Li, “Support Vector Regression for the Relationships between Ground Motion Parameters and Macroseismic Intensity in the Sichuan–Yunnan Region,” Applied Sciences, vol. 10, no. 9, p. 3086, Apr. 2020, doi: 10.3390/app10093086.
M. Wieland, W. Liu, and F. Yamazaki, “Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes,” Remote Sens (Basel), vol. 8, no. 10, p. 792, Sep. 2016, doi: 10.3390/rs8100792.
I. Aljarah, A. M. Al-Zoubi, H. Faris, M. A. Hassonah, S. Mirjalili, and H. Saadeh, “Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm,” Cognit Comput, vol. 10, no. 3, pp. 478–495, Jun. 2018, doi: 10.1007/s12559-017-9542-9.
P. Hajikhodaverdikhan, M. Nazari, M. Mohsenizadeh, S. Shamshirband, and K. Chau, “Earthquake prediction with meteorological data by particle filter-based support vector regression,” Engineering Applications of Computational Fluid Mechanics, vol. 12, no. 1, pp. 679–688, Jan. 2018, doi: 10.1080/19942060.2018.1512010.
A. Jufriansah, A. Anggraini, Z. Zulfakriza, A. Khusnani, and Y. Pramudya, “Forecast earthquake precursor in the Flores Sea,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 32, no. 3, p. 1825, Dec. 2023, doi: 10.11591/ijeecs.v32.i3.pp1825-1836.
Copyright (c) 2023 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
