DOI : https://doi.org/10.18517/ijaseit.12.1.14237

Tsunami Potential Prediction using Seismic Features and Artificial Neural Network for Tsunami Early Warning System

Astri Novianty (1), Carmadi Machbub (2), Sri Widiyantoro (3), Irwan Meilano (4), - Daryono (5)
(1) School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl Ganesha 10 Bandung, 40116, Indonesia
(2) School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl Ganesha 10 Bandung, 40116, Indonesia
(3) Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology, Jl Ganesha 10 Bandung, 40116, Indonesia
(4) Faculty of Earth Sciences and Technology, Bandung Institute of Technology, Jl Ganesha 10 Bandung, 40116, Indonesia
(5) Indonesian Agency for Meteorology Climatology and Geophysics, Jl. Angkasa 1 No. 2 Jakarta, 10610, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Novianty, Astri, et al. “Tsunami Potential Prediction Using Seismic Features and Artificial Neural Network for Tsunami Early Warning System”. International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 1, Jan. 2022, pp. 16-22, doi:10.18517/ijaseit.12.1.14237.
Citation Format :
Tsunamis are categorized as geophysical disasters because tectonic earthquakes triggered most of their occurrences. The high number of deaths due to tsunami catastrophe has made many countries develop a tsunami early warning system (TEWS), especially countries prone to tectonic earthquakes. One of the crucial subsystems in a TEWS is the tsunami potential prediction subsystem. To provide an early warning of tsunami, the prediction must be carried out based on early observation of the seismic event before the tsunami. In this short time of computation, the calculation of seismic parameters can only produce some roughly estimated features. Hence, a proper inference method that can decide accurate predictions upon the features is urgently needed for the TEWS. Some existing TEWSs are using rule-based inference to decide the prediction and often overestimate the prediction of tsunami potential. This study tries to develop a tsunami-potential prediction system using the machine learning approach as its inference method. Seismic features extracted from P-wave seismic signals are used as input data for learning and classification using a backpropagation artificial neural network (ANN). The accuracy result is then validated by K-fold cross-validation. Our simulation results show that the utilization of backpropagation ANN has given better accuracy in tsunami prediction compared to one of the existing TEWS that does not use machine learning for its prediction. At least for some seismic events that occurred during 2010-2017, the proposed system results in fewer overestimated predictions than the existing TEWS referred.

F. A. Ismail, A. Hakam, and T. Ophiyandri, “Earthquake safe houses training for tsunami preparedness in West Sumatra,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 1, pp. 318-324, 2020, doi: 10.18517/ijaseit.10.1.7850.

S. Koshimura, L. Moya, E. Mas, and Y. Bai, “Tsunami damage detection with remote sensing: A review,” Geosci., vol. 10, no. 5, pp. 1-28, 2020, doi: 10.3390/geosciences10050177.

A. Lomax and A. Michelini, “Mwpd: A duration-amplitude procedure for rapid determination of earthquake magnitude and tsunamigenic potential from P waveforms,” Geophys. J. Int., vol. 176, no. 1, pp. 200-214, 2009, doi: 10.1111/j.1365-246X.2008.03974. x.

R. Atika, A. E. Raditya, R. N. Marjianto, and H. S. Pramono, “Automatic Tsunami Early Warning System Based on Open Data of Indonesia Agency for Meteorological, Climatological, and Geophysics,” J. Phys. Conf. Ser., vol. 1413, no. 1, 2019, doi: 10.1088/1742-6596/1413/1/012012.

S. Harig et al., “The Tsunami Scenario Database of the Indonesia Tsunami Early Warning System (InaTEWS): Evolution of the Coverage and the Involved Modeling Approaches,” Pure Appl. Geophys., vol. 177, no. 3, pp. 1379-1401, 2020, doi: 10.1007/s00024-019-02305-1.

A. Fauzi and N. Mizutani, “Machine Learning Algorithms for Real-time Tsunami Inundation Forecasting: A Case Study in Nankai Region,” Pure Appl. Geophys., vol. 177, no. 3, pp. 1437-1450, 2020, doi: 10.1007/s00024-019-02364-4.

A. Pratondo, C. K. Chui, and S. H. Ong, “Integrating machine learning with region-based active contour models in medical image segmentation,” J. Vis. Commun. Image Represent., vol. 43, no. 1, pp. 1-9, 2017, doi: 10.1016/j.jvcir.2016.11.019.

M. A. Meier et al., “Reliable Real-Time Seismic Signal/Noise Discrimination with Machine Learning,” J. Geophys. Res. Solid Earth, vol. 124, no. 1, pp. 788-800, 2019, doi: 10.1029/2018JB016661.

N. A. Hitam and A. R. Ismail, “Comparative performance of machine learning algorithms for cryptocurrency forecasting,” Indones. J. Electr. Eng. Comput. Sci., vol. 11, no. 3, pp. 1121-1128, 2018, doi: 10.11591/ijeecs.v11.i3.pp1121-1128.

S. Naseer et al., “Enhanced network anomaly detection based on deep neural networks,” IEEE Access, vol. 6, pp. 48231-48246, 2018, doi: 10.1109/ACCESS.2018.2863036.

W. Wang et al., “A systematic review of machine learning models for predicting outcomes of stroke with structured data,” PLoS One, vol. 15, no. 6, pp. 1-16, 2020, doi: 10.1371/journal.pone.0234722.

L. Huang, J. Li, H. Hao, and X. Li, “Micro-seismic event detection and location in underground mines by using Convolutional Neural Networks (CNN) and deep learning,” Tunn. Undergr. Sp. Technol., vol. 81, no. June, pp. 265-276, 2018, doi: 10.1016/j.tust.2018.07.006.

M. Malfante, M. D. Mura, J. Mí©taxian, and J. I. Mars, “Machine Learning for Volcano-Seismic Signals,” IEEE Signal Process. Mag., vol. 35, no. 2, pp. 20-30, 2018.

Q. Kong, D. T. Trugman, Z. E. Ross, M. J. Bianco, B. J. Meade, and P. Gerstoft, “Machine learning in seismology: Turning data into insights,” Seismol. Res. Lett., vol. 90, no. 1, pp. 3-14, 2019, doi: 10.1785/0220180259.

W. Li, N. Narvekar, N. Nakshatra, N. Raut, B. Sirkeci, and J. Gao, “Seismic data classification using machine learning,” Proc. - IEEE 4th Int. Conf. Big Data Comput. Serv. Appl. BigDataService 2018, pp. 56-63, 2018, doi: 10.1109/BigDataService.2018.00017.

Z. E. Ross, M. A. Meier, E. Hauksson, and T. H. Heaton, “Generalized seismic phase detection with deep learning,” Bull. Seismol. Soc. Am., vol. 108, no. 5, pp. 2894-2901, 2018, doi: 10.1785/0120180080.

Z. E. Ross, Y. Yue, M. A. Meier, E. Hauksson, and T. H. Heaton, “PhaseLink: A Deep Learning Approach to Seismic Phase Association,” J. Geophys. Res. Solid Earth, vol. 124, no. 1, pp. 856-869, 2019, doi: 10.1029/2018JB016674.

T. Perol, M. Gharbi, and M. A. Denolle, “Convolutional neural network for earthquake detection and location,” Sci. Adv., vol. 4, no. 2, 2018, doi: 10.1126/sciadv.1700578.

G. Pughazhendhi, A. Raja, P. Ramalingam, and D. K. Elumalai, “Earthosys—tsunami prediction and warning system using machine learning and IoT,” in Proceedings of International Conference on Computational Intelligence and Data Engineering, 2019, vol. 28, pp. 103-113, doi: 10.1007/978-981-13-6459-4_12.

A. Novianty, C. Machbub, S. Widiyantoro, I. Meilano, and H. Irawan, “Tsunami potential identification based on seismic features using KNN algorithm,” Proceeding - 2019 IEEE 7th Conf. Syst. Process Control. ICSPC 2019, no. December, pp. 155-160, 2019, doi: 10.1109/ICSPC47137.2019.9068095.

F. Martí­nez-ílvarez and A. Morales-Esteban, “Big data and natural disasters: New approaches for spatial and temporal massive data analysis,” Comput. Geosci., vol. 129, no. May, pp. 38-39, 2019, doi: 10.1016/j.cageo.2019.04.012.

H. Jia, J. Lin, and J. Liu, “An earthquake fatalities assessment method based on feature importance with deep learning and random forest models,” Sustain., vol. 11, no. 10, 2019, doi: 10.3390/su11102727.

O. I. Abiodun et al., “Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition,” IEEE Access, vol. 7, pp. 158820-158846, 2019, doi: 10.1109/ACCESS.2019.2945545.

A. Kundu, Y. S. Bhadauria, S. Basu, and S. Mukhopadhyay, “Application of ANN and SVM for identification of tsunamigenic earthquakes from 3-component seismic data,” RTEICT 2017 - 2nd IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol. Proc., vol. 2018-Janua, no. 2, pp. 10-13, 2017, doi: 10.1109/RTEICT.2017.8256549.

M. Syifa, P. R. Kadavi, and C. W. Lee, “An artificial intelligence application for post-earthquake damage mapping in Palu, central Sulawesi, Indonesia,” Sensors (Switzerland), vol. 19, no. 3, 2019, doi: 10.3390/s19030542.

Y. Bai et al., “A framework of rapid regional tsunami damage recognition from post-event terraSAR-X imagery using deep neural networks,” IEEE Geosci. Remote Sens. Lett., vol. 15, no. 1, pp. 43-47, 2018, doi: 10.1109/LGRS.2017.2772349.

GFZ German Research Centre for Geosciences, “GEOFON and EIDA Data Archives,” 2013. http://eida.gfz-potsdam.de/webdc3/ (accessed Jul. 10, 2018).

Incorporated Research Institutions for Seismology (IRIS), “Wilber 3: Select Event,” 2010. http://ds.iris.edu/wilber3/find_event (accessed Jul. 10, 2018).

I. Dumke and C. Berndt, “Prediction of seismic p-wave velocity using machine learning,” Solid Earth, vol. 10, no. 6, pp. 1989-2000, 2019, doi: 10.5194/se-10-1989-2019.

Y. M. Wu and H. Kanamori, “Experiment on an onsite early warning method for the Taiwan early warning system,” Bull. Seismol. Soc. Am., vol. 95, no. 1, pp. 347-353, 2005, doi: 10.1785/0120040097.

M. Picozzi, D. Bindi, P. Brondi, D. Di Giacomo, S. Parolai, and A. Zollo, “Rapid determination of P wave-based energy magnitude: Insights on source parameter scaling of the 2016 Central Italy earthquake sequence,” Geophys. Res. Lett., vol. 44, no. 9, pp. 4036-4045, 2017, doi: 10.1002/2017GL073228.

M. S. Ghanim and K. Shaaban, “Estimating Turning Movements at Signalized,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 5, pp. 1828-1836, 2019.

N. Salleh, S. S. Yuhaniz, S. F. Sabri, and N. F. M. Azmi, “Enhancing prediction method of ionosphere for space weather monitoring using machine learning approaches: A review,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 1, pp. 9-15, 2020, doi: 10.18517/ijaseit.10.1.10163.

T. T. Wong and N. Y. Yang, “Dependency Analysis of Accuracy Estimates in k-Fold Cross Validation,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 11, pp. 2417-2427, 2017, doi: 10.1109/TKDE.2017.2740926.

T. T. Wong and P. Y. Yeh, “Reliable Accuracy Estimates from k-Fold Cross Validation,” IEEE Trans. Knowl. Data Eng., vol. 32, no. 8, pp. 1586-1594, 2020, doi: 10.1109/TKDE.2019.2912815.

National Geophysical Data Center / World Data Service (NGDC/WDS), “NGDC/WDS Global Historical Tsunami Database,” 2010. https://www.ngdc.noaa.gov/hazard/tsu_db.shtml (accessed Sep. 01, 2018).

Madlazim, “Assessment of tsunami generation potential through rapid analysis of seismic parameters: Case study: Comparison of the Sumatra Earthquakes of 6 April and 25 October 2010,” Sci. Tsunami Hazards, vol. 32, no. 1, pp. 29-38, 2013.

UNESCO Intergovernmental Oceanographic Comission, “2nd March 2016 Southwest of Sumatra Earthquake and Tsunami Event: Post-event Assesment of the Performance of the Indian Ocean Tsunami Warning and Mitigation System,” Paris, 2017.

UNESCO Indian Ocean Tsunami Warning and Mitigation System (IOTWS), “Tsunami Warning and Mitigation Systems to protect Coastal Communities,” Paris, 2015.

Agency for Meteorology Climatology and Geophysics Indonesia (BMKG), “Indonesia Tsunami Service Provider.” http://rtsp.bmkg.go.id/ (accessed Feb. 07, 2018).

D. Hartanto and T. Yatinamantoro, “Indonesia Tsunami Early Warning System (InaTEWS).” http://jexp.main.jp/h24soukai/Indonesia.pdf (accessed Feb. 07, 2018).

“Magnitude 7.4 earthquake rattles western Indonesia, 9 May 2010,” The Independent News, 2010. http://www.independent.co.uk/news/world/asia/magnitude-74-earthquake-rattles-western-indonesia-1969787.html (accessed Feb. 10, 2018).

“Earthquakes kill three in Indonesia, 16 June 2010,” Brisbane Times, 2010. https://www.brisbanetimes.com.au/world/earthquakes-kill-three-in-indonesia-20100616-yfqw.html (accessed Feb. 12, 2018).

Community Tsunami Early-Warning Center, “September 29th 2010.” http://www.communitytsunamiwarning.com/page20.htm (accessed Feb. 12, 2018).

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).