Analysis of earthquake forecasting using random forest
Article Sidebar
Main Article Content
Abstract
The subject of forecasting earthquakes is an intriguing one to investigate. As a natural calamity, earthquakes continue to be devastating, not just to the economy but also to the lives of individuals. This gave rise to the concept of creating an early warning system against seismic catastrophes to minimize deaths. Researchers have been making earthquake forecasts and seismic hazard ratings of a location for a few years now. In this work, we attempt to forecast earthquakes before they occur using p-arrival data, which includes information on disaster arrival time and amplitude height from the arrival station. Several studies on earthquake prediction have been carried out so far and have developed and used the Random Forest method and one of the Machine Learning. According to [1], the process of predicting earthquakes has been studied for a long time, but there is still uncertainty due to the diversity and complexity of the earthquake phenomenon itself. According to [2], conducting a random forest prediction model to identify the structural safety status of buildings damaged by the earthquake is probabilistic. An earthquake's latitude, longitude, magnitude, and depth may be predicted using the random forest algorithm. A random forest with multioutput technique is employed, with variables being each station's recorded value and geographic position. This study's predictions were accurate to within 63 percent.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
T. H. Jordan et al., “Operational earthquake forecasting: State of knowledge and guidelines for utilization,” Ann. Geophys., vol. 54, no. 4, pp. 319–391, 2011, doi: 10.4401/ag-5350.
A. Barkat et al., “Time series analysis of soil radon in Northern Pakistan: Implications for earthquake forecasting,” Appl. Geochemistry, vol. 97, pp. 197–208, 2018, doi: 10.1016/j.apgeochem.2018.08.016.
T. Hardy, B. Nurdiyanto, D. Ngadmanto, P. Susilanto, and B. Sunardi, “Kajian Kerawanan Gempabumi Berbasis Sig Dalam Upaya Mitigasi Bencana Studi Kasus Kabupaten Dan Kota Sukabumi,” 2012, [Online]. Available: https://publikasiilmiah.ums.ac.id/handle/11617/1422%0Ahttps://publikasiilmiah.ums.ac.id/bitstream/handle/11617/1422/5-SNPJ-SIG-2012-Bambang Sunardi.pdf?sequence=1&isAllowed=y.
G. Asencio-Cortés, A. Morales-Esteban, X. Shang, and F. Martínez-Álvarez, “Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure,” Comput. Geosci., vol. 115, pp. 198–210, 2018, doi: 10.1016/j.cageo.2017.10.011.
Y. Ogata, “A prospect of earthquake prediction research,” Stat. Sci., vol. 28, no. 4, pp. 521–541, 2013, doi: 10.1214/13-STS439.
G. Cremen and C. Galasso, “Earthquake early warning: Recent advances and perspectives,” Earth-Science Rev., vol. 205, no. February, p. 103184, 2020, doi: 10.1016/j.earscirev.2020.103184.
E. E. H. Doyle et al., “Interpretations of aftershock advice and probabilities after the 2013 Cook Strait earthquakes, Aotearoa New Zealand,” Int. J. Disaster Risk Reduct., vol. 49, p. 101653, 2020, doi: 10.1016/j.ijdrr.2020.101653.
M. C. Mariani, M. A. M. Bhuiyan, O. K. Tweneboah, and H. Gonzalez-Huizar, “Long memory effects and forecasting of earthquake and volcano seismic data,” Phys. A Stat. Mech. its Appl., vol. 559, p. 125049, 2020, doi: 10.1016/j.physa.2020.125049.
K. Trevlopoulos, P. Guéguen, A. Helmstetter, and F. Cotton, “Earthquake risk in reinforced concrete buildings during aftershock sequences based on period elongation and operational earthquake forecasting,” Struct. Saf., vol. 84, no. February 2019, p. 101922, 2020, doi: 10.1016/j.strusafe.2020.101922.
H. Yepes, J. M. Nocquet, B. Bernard, P. B. Palacios, S. Vaca, and S. Aguaiza, “Comments on the paper ‘Two independent real-time precursors of the 7.8 M earthquake in Ecuador based on radioactive and geodetic processes – Powerful tools for an early warning system’ by Toulkeridis et al. (2019),” J. Geodyn., vol. 133, p. 101648, 2020, doi: 10.1016/j.jog.2019.101648.
D. Bindi, I. Iervolino, and S. Parolai, “On-site structure-specific real-time risk assessment: perspectives from the REAKT project,” Bull. Earthq. Eng., vol. 14, no. 9, pp. 2471–2493, 2016, doi: 10.1007/s10518-016-9889-4.
M. Herrmann, J. D. Zechar, and S. Wiemer, “Communicating time-varying seismic risk during an earthquake sequence,” Seismol. Res. Lett., vol. 87, no. 2A, pp. 301–312, 2016, doi: 10.1785/0220150168.
K. Budiman and I. Akhlis, “Changing user needs and motivation to visit a website through ad experience: A case study of a university website,” J. Phys. Conf. Ser., vol. 1918, no. 4, 2021, doi: 10.1088/1742-6596/1918/4/042008.
K. Budiman, S. Subhan, and D. A. Efrilianda, “Business Process re-engineering to support the sustainability of the construction industry and sales commodities in large scale transaction during Covid 19 with integrating ERP and Quotation System,” Sci. J. Informatics, vol. 8, no. 1, pp. 84–91, 2021, doi: 10.15294/sji.v8i1.27969.
Sugianto, Z. Abidin, A. T. Putra, and K. Budiman, “Knowledge management system in a higher education institution: Development of an expertise search system,” J. Phys. Conf. Ser., vol. 1918, no. 4, 2021, doi: 10.1088/1742-6596/1918/4/042021.
K. Budiman, A. T. Putra, Alamsyah, E. Sugiharti, M. A. Muslim, and R. Arifudin, “Implementation of ERP system functionalities for data acquisition based on API at the study program of Universities,” J. Phys. Conf. Ser., vol. 1918, no. 4, 2021, doi: 10.1088/1742-6596/1918/4/042151.
J. W. Baker, “Measuring bias in structural response caused by ground motion scaling,” Pacific Conf. Earthq. Eng., no. 056, pp. 1–6, 2007, doi: 10.1002/eqe.
Y. Zhang, H. V. Burton, H. Sun, and M. Shokrabadi, “A machine learning framework for assessing post-earthquake structural safety,” Struct. Saf., vol. 72, pp. 1–16, 2018, doi: 10.1016/j.strusafe.2017.12.001.
Y. Reuland, P. Lestuzzi, and I. F. C. Smith, “A model-based data-interpretation framework for post-earthquake building assessment with scarce measurement data,” Soil Dyn. Earthq. Eng., vol. 116, no. August 2018, pp. 253–263, 2019, doi: 10.1016/j.soildyn.2018.10.008.
S. Grimaz and P. Malisan, “How could cumulative damage affect the macroseismic assessment?,” Bull. Earthq. Eng., vol. 15, no. 6, pp. 2465–2481, 2017, doi: 10.1007/s10518-016-0016-3.
M. Brun, P. Labbe, D. Bertrand, and A. Courtois, “Pseudo-dynamic tests on low-rise shear walls and simplified model based on the structural frequency drift,” Eng. Struct., vol. 33, no. 3, pp. 796–812, 2011, doi: 10.1016/j.engstruct.2010.12.003.
P. Probst and A. L. Boulesteix, “To tune or not to tune the number of trees in random forest,” J. Mach. Learn. Res., vol. 18, pp. 1–8, 2018.
P. Probst, M. N. Wright, and A. L. Boulesteix, “Hyperparameters and tuning strategies for random forest,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 9, no. 3, pp. 1–15, 2019, doi: 10.1002/widm.1301.
M. Pal, “Random forest classifier for remote sensing classification,” Int. J. Remote Sens., vol. 26, no. 1, pp. 217–222, 2005, doi: 10.1080/01431160412331269698.
Y. Qi, “Ensemble Machine Learning,” Ensemble Mach. Learn., pp. 307–323, 2012, doi: 10.1007/978-1-4419-9326-7.
V. Svetnik, A. Liaw, C. Tong, J. Christopher Culberson, R. P. Sheridan, and B. P. Feuston, “Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling,” J. Chem. Inf. Comput. Sci., vol. 43, no. 6, pp. 1947–1958, 2003, doi: 10.1021/ci034160g.
Y. N. Ifriza, C. E. Edi, and J. E. Suseno, “expert system irrigation management of agricultural reservoir system using analytical hierarchy process ( AHP ) and forward chaining method,” pp. 74–83, 2017.
M. Sam and Y. N. Ifriza, “A combination of TDM and KSAM to determine initial feasible solution of transportation problems,” J. Soft Comput. Explor., vol. 2, no. 1, pp. 17–24, 2021, doi: 10.52465/joscex.v2i1.16.
Y. N. Ifriza and M. Sam, “Irrigation management of agricultural reservoir with correlation feature selection based binary particle swarm optimization,” J. Soft Comput. Explor., vol. 2, no. 1, pp. 40–45, 2021, doi: 10.52465/joscex.v2i1.23.
S. Gentili and R. Di Giovambattista, “Forecasting strong aftershocks in earthquake clusters from northeastern Italy and western Slovenia,” Phys. Earth Planet. Inter., vol. 303, no. September 2019, p. 106483, 2020, doi: 10.1016/j.pepi.2020.106483.
A. Zarola and A. Sil, “Forecasting of future earthquakes in the northeast region of India considering energy released concept,” Comput. Geosci., vol. 113, pp. 1–13, 2018, doi: 10.1016/j.cageo.2018.01.003.
Y. Zhu, F. Liu, G. Zhang, and Y. Xu, “Development and prospect of mobile gravity monitoring and earthquake forecasting in recent ten years in China,” Geod. Geodyn., vol. 10, no. 6, pp. 485–491, 2019, doi: 10.1016/j.geog.2019.05.006.
Q. Hong, S. Crampin, and Y. Gao, “Changes in shear-wave splitting at the 2014 Bárðarbunga seismic crisis and dyke intrusion in Iceland compared with earthquakes and other eruptions,” Phys. Earth Planet. Inter., vol. 300, no. February, p. 106446, 2020, doi: 10.1016/j.pepi.2020.106446.
M. Akhoondzadeh, “Decision Tree, Bagging and Random Forest methods detect TEC seismo-ionospheric anomalies around the time of the Chile, (M w = 8.8) earthquake of 27 February 2010,” Adv. Sp. Res., vol. 57, no. 12, pp. 2464–2469, 2016, doi: 10.1016/j.asr.2016.03.035.
M. Battarra, B. Balcik, and H. Xu, “Disaster preparedness using risk-assessment methods from earthquake engineering,” Eur. J. Oper. Res., vol. 269, no. 2, pp. 423–435, 2018, doi: 10.1016/j.ejor.2018.02.014.
R. Jena and B. Pradhan, “Integrated ANN-cross-validation and AHP-TOPSIS model to improve earthquake risk assessment,” Int. J. Disaster Risk Reduct., vol. 50, p. 101723, 2020, doi: 10.1016/j.ijdrr.2020.101723.
K. Nikolopoulos, F. Petropoulos, V. S. Rodrigues, S. Pettit, and A. Beresford, “A disaster response model driven by spatial–temporal forecasts,” Int. J. Forecast., no. xxxx, 2020, doi: 10.1016/j.ijforecast.2020.01.002.
C. Hibert, F. Provost, J. P. Malet, A. Maggi, A. Stumpf, and V. Ferrazzini, “Automatic identification of rockfalls and volcano-tectonic earthquakes at the Piton de la Fournaise volcano using a Random Forest algorithm,” J. Volcanol. Geotherm. Res., vol. 340, pp. 130–142, 2017, doi: 10.1016/j.jvolgeores.2017.04.015.
A. Bagriacik, R. A. Davidson, M. W. Hughes, B. A. Bradley, and M. Cubrinovski, “Comparison of statistical and machine learning approaches to modeling earthquake damage to water pipelines,” Soil Dyn. Earthq. Eng., vol. 112, no. June 2017, pp. 76–88, 2018, doi: 10.1016/j.soildyn.2018.05.010.
P. Jiang, X. Liu, and M. Zheng, “Emergency blood demand forecasting after earthquakes,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 773–777, 2019, doi: 10.1016/j.ifacol.2019.11.209.