Analysis and Prediction of the Occurrence of an Earthquake Using ARIMA and Statistical Tests
Rabbani Nur Kumoro(1*), Audrey Shafira Fattima(2), William Hilmy Susatyo(3), Dzikri Rahadian Fudholi(4)
(1) Program Studi Ilmu Komputer FMIPA UGM, Yogyakarta
(2) Program Studi Ilmu Komputer FMIPA UGM, Yogyakarta
(3) Program Studi Ilmu Komputer FMIPA UGM, Yogyakarta
(4) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Earthquakes present significant risks to both human safety and infrastructure, emphasizing the need for precise prediction models to minimize their adverse effects. This study seeks to tackle the challenge of accurately forecasting the occurrence time of earthquakes by utilizing the LANL Earthquake dataset, which comprises seismic signals from a laboratory model emulating tectonic faults. In this study, we employed the ARIMA model and compared it with Linear Regression to predict earthquake occurrences. Our findings demonstrate that the ARIMA (1,1,1) model surpasses other models, achieving the lowest MAE of 0.110628. The validity of the model's assumptions is confirmed through the Ljung-Box and Jarque-Bera tests, which verify the absence of autocorrelation and the normal distribution of residuals, respectively.
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N. Christiastuti, “Korban Jiwa GEMPA Turki-Suriah Bertambah jadi 41.000 orang,” detiknews, https://news.detik.com/internasional/d-6569601/korban-jiwa-gempa-turki-suriah-bertambah-jadi-41-000-orang (accessed Feb. 15, 2023). [2] K. Hirose, S. Labrosse, and J. Hernlund, “Composition and state of the Core,” Annual Review of Earth and Planetary Sciences, vol. 41, no. 1, pp. 657–691, 2013. doi:10.1146/annurev-earth-050212-124007. [3] A. Howard, B. Rouet-Leduc, and L. J. Pyrak-Nolte, “Lanl earthquake prediction,” Kaggle, https://kaggle.com/competitions/LANL-Earthquake-Prediction (accessed Feb. 7, 2023). [4] P. A. Johnson et al., “Laboratory earthquake forecasting: A machine learning competition,” Proceedings of the National Academy of Sciences, vol. 118, no. 5, 2021. doi:10.1073/pnas.2011362118. [5] E. Paparoditis and D. N. Politis, “The asymptotic size and power of the augmented dickey–fuller test for a unit root,” Econometric Reviews, vol. 37, no. 9, pp. 955–973, 2016. doi:10.1080/00927872.2016.1178887. [6] A. K.P, A. S. Oluwaseun, and V. G. Jemilohun, “Test for Stationarity on Inflation Rates in Nigeria using Augmented Dickey Fuller Test and Phillips-Persons Test,” IOSR Journal of Mathematics, vol. 16, no. 3, pp. 11–14, 2020. doi:10.9790/5728-1603031114. [7] X. Wang, Y. Kang, R. J. Hyndman, and F. Li, “Distributed Arima models for ultra-long Time Series,” International Journal of Forecasting, vol. 39, no. 3, pp. 1163–1184, 2023. doi:10.1016/j.ijforecast.2022.05.001. [8] B. Dey, B. Roy, S. Datta, and T. S. Ustun, “Forecasting ethanol demand in India to meet future blending targets: A comparison of Arima and various regression models,” Energy Reports, vol. 9, pp. 411–418, 2023. doi:10.1016/j.egyr.2022.11.038. [9] I. Unggara, A. Musdholifah, and A. K. Sari, “Optimization of Arima forecasting model using Firefly algorithm,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 13, no. 2, p. 127, 2019. doi:10.22146/ijccs.37666. [10] G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, “Linear regression,” Springer Texts in Statistics, pp. 69–134, 2023. doi:10.1007/978-3-031-38747-0_3. [11] H. K. Prakosa and N. Rokhman, “Anomaly detection in hospital claims using K-means and linear regression,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 15, no. 4, p. 391, 2021. doi:10.22146/ijccs.68160. [12] L. Wynants et al., “Prediction models for diagnosis and prognosis of covid-19: Systematic Review and Critical Appraisal,” BMJ, p. m1328, 2020. doi:10.1136/bmj.m1328. [13] S. M. Robeson and C. J. Willmott, “Decomposition of the mean absolute error (mae) into systematic and unsystematic components,” PLOS ONE, vol. 18, no. 2, 2023. doi:10.1371/journal.pone.0279774. [14] M. D. Fauzi, A. E. Putra, and W. Wahyono, “Estimation of average car speed using the haar-like feature and Correlation Tracker method,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 14, no. 4, p. 353, 2020. doi:10.22146/ijccs.57262. [15] H. Hassani and M. R. Yeganegi, “Selecting optimal lag order in ljung–box test,” Physica A: Statistical Mechanics and its Applications, vol. 541, p. 123700, 2020. doi:10.1016/j.physa.2019.123700. [16] D. Abdellatif, K. El Moutaouakil, and K. Satori, “Clustering and Jarque-bera normality test to face recognition,” Procedia Computer Science, vol. 127, pp. 246–255, 2018. doi:10.1016/j.procs.2018.01.120.
DOI: https://doi.org/10.22146/ijccs.90202
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