Data Mining Earthquake Prediction with Multivariate Adaptive Regression Splines and Peak Ground Acceleration
Keywords:
Earthquake hazard prediction, Function base, Predictor variable, Peak ground acceleration, Nonparametric regression
Abstract
Earthquake research has not yielded promising results because earthquakes have uncertain data parameters, and one of the methods to overcome the problem of uncertain parameters is the nonparametric method, namely Multivariate Adaptive Regression Splines (MARS). Sumbawa Island is part of the territory of Indonesia and is in the position of three active earth plates, so Sumbawa is prone to earthquake hazards. Therefore, this research is important to do. This study aimed to analyze earthquake hazard prediction on the island of Sumbawa by using the nonparametric MARS and Peak Ground Acceleration (PGA) methods to determine the risk of earthquake hazards. The method used in this study was MARS, which has two completed stages: Forward Stepwise and Backward Stepwise. The results of this study were based on testing and parameter analysis obtained a Mathematical model with 11 basis functions (BF) that contribute to the response variable, namely (BF) 1,2,3,4,5,7,9,11, and the basis functions do not contribute 6, 8, and 10. The predictor variables with the greatest influence were 100% Epicenter Distance and 73.8% Magnitude. The conclusion of this study is based on the highest PGA values in the areas most prone to earthquake hazards in Sumbawa, namely Mapin Kebak, Mapin Rea, Pulau Panjang, and Pulau Saringi.
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Prediction. Cham: Springer International Publishing, 2022, no. March.
[3] P. Dubey, “Analysis of Clustering-Algorithms for Efficient Data Mining,” no. April, 2023.
[4] A. O¨ zmen, “Multi-objective regression modeling for natural gas prediction with ridge regression and CMARS,” International
Journal of Optimization and Control: Theories and Applications, vol. 12, no. 1, pp. 56–65, 2022.
[5] A. O¨ zmen, G. W. Weber, and I. Batmaz, “The new Robust CMARS (RCMARS) method,” in 24th Mini EURO Conference on
Continuous Optimization and Information-Based Technologies in the Financial Sector, MEC EurOPT 2010, no. June, 2010, pp.
362–368.
[6] J. Zhen, F. J. de Ruiter, E. Roos, and D. den Hertog, “Robust Optimization for Models with Uncertain Second-Order Cone and
Semidefinite Programming Constraints,” INFORMS Journal on Computing, vol. 34, no. 1, pp. 196–216, 2022.
[7] K. M. Asim, A. Idris, T. Iqbal, and F. Mart´ınez-A´ lvarez, “Earthquake prediction model using support vector regressor and
hybrid neural networks,” PLOS ONE, vol. 13, no. 7, pp. 1–22, jul 2018.
[8] T. A. Adagunodo, S. L¨uning, A. M. Adeleke, J. O. Omidiora, A. P. Aizebeokhai, K. D. Oyeyemi, and O. S. Hammed, “Evaluation
of 0 M 8 earthquake data sets in African Asian region during 19662015,” Data in Brief, vol. 17, no. April, pp. 588–603,
apr 2019.
[9] B. Sadhukhan, S. Chakraborty, S. Mukherjee, and R. K. Samanta, “Climatic and seismic data-driven deep learning model for
earthquake magnitude prediction,” Frontiers in Earth Science, vol. 11, no. February, pp. 1–24, feb 2023.
[10] X. Zhang, W. ReichardFlynn, M. Zhang, M. Hirn, and Y. Lin, “Spatiotemporal Graph Convolutional Networks for Earthquake
Source Characterization,” Journal of Geophysical Research: Solid Earth, vol. 127, no. 11, nov 2022.
[11] 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,” International Journal on Informatics Visualization, vol. 2, no. 1, pp. 37–43, 2018.
[12] R. Zakaria, A. N. Jifrin, S. N. Jaman, and R. Roslee, “Fuzzy Interpolation Curve Modelling of Earthquake Magnitude Data,”
IOP Conference Series: Earth and Environmental Science, vol. 1103, no. 1, pp. 1–12, nov 2022.
[13] X. Jianjun, Y. Bingjie, and W. Rongji, “Study on China’s Earthquake Prediction by Mathematical Analysis and its Application
in Catastrophe Insurance,” IOP Conference Series: Earth and Environmental Science, vol. 128, no. 1, pp. 1–7, mar 2019.
[14] J. Fayaz, Y. Xiang, and F. Zareian, “Generalized ground motion prediction model using hybrid recurrent neural network,”
Earthquake Engineering and Structural Dynamics, vol. 50, no. 6, pp. 1539–1561, 2021.
[15] D.-C. Feng, X.-Y. Cao, D. Wang, and G. Wu, “A PDEM-based non-parametric seismic fragility assessment method for RC
structures under non-stationary ground motions,” Journal of Building Engineering, vol. 63, no. January, pp. 1–17, jan 2023.
[16] G. Zheng, P. Yang, H. Zhou, C. Zeng, X. Yang, X. He, and X. Yu, “Evaluation of the earthquake induced uplift displacement of
tunnels using multivariate adaptive regression splines,” Computers and Geotechnics, vol. 113, p. 103099, sep 2019.
[17] D. Priyanto, M. Zarlis, H. Mawengkang, and S. Efendi, “Analysis of earthquake hazards prediction with multivariate adaptive
regression splines,” International Journal of Electrical and Computer Engineering, vol. 12, no. 3, pp. 2885–2893, 2022.
[18] C. K. Arthur, V. A. Temeng, and Y. Y. Ziggah, “Multivariate Adaptive Regression Splines (MARS) approach to blast-induced
ground vibration prediction,” International Journal of Mining, Reclamation and Environment, vol. 34, no. 3, pp. 198–222, 2020.
[19] S. D. Prihastuti Yasmirullah, B. W. Otok, J. D. Trijoyo Purnomo, and D. D. Prastyo, “Modification of Multivariate Adaptive
Regression Spline (MARS),” Journal of Physics: Conference Series, vol. 1863, no. 1, pp. 1–12, mar 2021.
[20] D. Pan, A. Zeng, L. Jia, Y. Huang, T. Frizzell, and X. Song, “Early Detection of Alzheimer’s Disease Using Magnetic Resonance
Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning,” Frontiers in Neuroscience,
vol. 14, no. May, pp. 1–19, may 2020.
[21] F. Ferraccioli, L. M. Sangalli, and L. Finos, “Nonparametric tests for semiparametric regression models,” TEST, no. June, pp.
1–25, jun 2023.
[22] D. Priyanto, M. Zarlis, H. Mawengkang, and S. Efendi, “Alternative approach to seismic hazards prediction using non parametric
adaptive regression method,” Journal of Theoretical and Applied Information Technology, vol. 98, no. 21, pp. 3425–3435,
2020.
[23] S. Xiang and W. Yao, “Nonparametric statistical learning based on modal regression,” Journal of Computational and Applied
Mathematics, vol. 409, no. January, pp. 1–15, aug 2022.
[24] E. Koken, “Yatay Milli Krclarda Krma Kapasitesinin Regresyon, Yapay Sinir Alar ve C¸ ok Deikenli Uyarlamal Regresyon
Analizi Kullanlarak Modellenmesi,” Afyon Kocatepe University Journal of Sciences and Engineering, vol. 22, no. 5, pp. 1193–
1203, 2022.
[25] A. Arafa, N. El-Fishawy, M. Badawy, and M. Radad, “RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing
imbalanced data classification,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, pp.
5059–5074, sep 2022.
[26] M. Rajif and S. Syafriani, “Hazard Seismic Zonation Analysis Of West Sumatra Region Using Probabilistic Hazard Seismic
Analysis (Phsa) Method,” Pillar or Physics, vol. 14, no. 1, pp. 8–17, jul 2021.
[27] A. Akinci, I. Munaf`o, and L. Malagnini, “S-Wave Attenuation Variation and its Impact on Ground Motion Amplitudes During
20162017 Central Italy Earthquake Sequence,” Frontiers in Earth Science, vol. 10, no. July, pp. 2011–2015, 2022.
[28] W. Haryadi, “Gempa Tektonik Di Pulau Sumbawa Dan Dampaknya Terhadap Bangunan Sipil (Suatu Kajian Geologis),” Ganec
Swara, vol. 6, no. 2, pp. 13–19, 2012.
[29] A. Sabtaji, “Statistics of Tectonic Earthquake Events Each Proovince in Indonesia Territory for 11 Years of Observation (2009-
2019),” Buletin Meteorologi, Klimatologi, dan Geofisika, vol. 1, no. 7, pp. 31–46, 2020.
[30] A. C. Hidajah, F. Febriyanti, and D. R. Faisal, “Faktor Risiko KLB Keracunan Makanan Pasca Gempa Bumi di Kabupaten
Sumbawa,” Jurnal Kesehatan, vol. 14, no. 2, pp. 65–70, 2021.
Published
2023-07-24
How to Cite
Priyanto, D., Triwijoyo, B., Jollyta, D., Hairani, H., & Dasriani, N. (2023). Data Mining Earthquake Prediction with Multivariate Adaptive Regression Splines and Peak Ground Acceleration. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(3), 583-592. https://doi.org/https://doi.org/10.30812/matrik.v22i3.3061
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