skip to main content

Algoritme deteksi kedatangan tsunami otomatis untuk sistem observasi tinggi muka air laut

Automatic tsunami arrival detection algorithm for sea level observation system

1Manado Geophysics Station, Meteorology Climatology and Geophysics Agency. Jl.Harapan 42, Manado, Sulawesi Utara 95161, Indonesia

2Master Program of Aquatic Sciences, Universitas Sam Ratulangi. Jl. Kampus, Bahu, Manado, Sulawesi Utara 95115, Indonesia

3Faculty of Fisheries and Marine Science, Universitas Sam Ratulangi. Jl. Kampus, Bahu, Manado, Sulawesi Utara 95115, Indonesia

Received: 12 Dec 2020; Revised: 21 May 2021; Accepted: 17 Jun 2021; Published: 31 Oct 2021.
Open Access Copyright (c) 2021 The authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
The automatic tsunami detection algorithm needs to be put in the sea level observation system to give society a quick warning when a tsunami happens. This study designs an automatic tsunami detection algorithm consisting of three sub-algorithm: spike elimination, gap data filling, and tsunami detection. Spike elimination and gap data filling are used to improve the sea level data, which is often disturbed by spikes and gap data due to electronic factors. This algorithm was tested using time-series tide gauge data that contain tsunami waveforms in Indonesia from 2007 to 2019. About 54.52 % of 409 spikes have been eliminated while the gap data were successfully filled. Furthermore, tsunami detection, which uses DART (Deep-ocean Assessment and Reporting of Tsunamis) and TEDA (Tsunami Early Detection Algorithm) methods, can detect 7 of 10 tsunami waveforms. However, there are three undetected tsunamis and one false detection. This algorithm has an average delay of 7.7 minutes in detection time.
Keywords: tsunami detection algorithm; spike elimination; gap data filling; tide gauge
Funding: Stasiun Geofisika Manado, Badan Meteorologi Klimatologi dan Geofisika;Universitas Sam Ratulangi

Article Metrics:

  1. J. Lauterjung and H. Letz (Eds.), 10 years Indonesian Tsunami Early Warning System: Experiences, lessons learned and outlook. Postdam: GFZ German Research Centre for Geosciences, 2017. doi: 10.2312/GFZ.7.1.2017.001
  2. UNDRR and UNESCO-IOC, Limitations and challenges of early warning system: A case study of the 2018 Palu-Donggala tsunami. Jakarta: UNDRR, 2019
  3. S. T. Grilli, et al., “Modelling of the tsunami from the December 22, 2018 lateral collapse of Anak Krakatau volcano in the Sunda straits, Indonesia,” Scientific Reports, vol. 9:11946, 2019. doi: 10.1038/s41598-019-48327-6
  4. M. Di Risio and G. M. Beltrami, “Algorithms for automatic, real-time tsunami detection in wind-wave measurements: using strategies and practical aspects,” Procedia Engineering, vol. 70, pp. 545–554, 2014. doi: 10.1016/j.proeng.2014.02.060
  5. H. O. Mofjeld, ”Tsunami detection algorithm,” NOAA Center for Tsunami Research, 1997. [Online]. Available: tda_ documentation.html [Accessed: March 27, 2020]
  6. F. Chierichi, D. Embriaco, and L. Pignagnoli, “A new real-time tsunami detection algorithm,” Journal of Geophysical Research: Oceans, vol. 122, no. 1, pp. 636-652, 2017. doi: 10.1002/2016JC012170
  7. L. Bressan and S. Tinti, “Structure and performance of a realtime algorithm to detect tsunami or tsunami-like alert conditions based on sea-level records analysis,” Natural Hazards and Earth System Sciences, vol. 11, no. 5, pp. 1499-1521, 2011. doi: 10.5194/nhess-11-1499-2011
  8. L. Bressan, F. Zaniboni, and S. Tinti, “Calibration of a real-time tsunami detection algorithm for sites with no instrumental tsunami records: application to coastal tide-gauge stations in eastern Sicily, Italy,” Natural Hazards Earth System Sciences, vol. 13, no. 12, pp. 3129-3144, 2013. doi: 10.5194/nhess-13-3129-2013
  9. J. Williams, A. Matthews, and S. Jevrejeva, “Development of an automatic tide gauge processing system,” National Oceanography Centre, Liverpool, Research and Consultancy Report No. 64, 2019. [Online]. Available: [Accessed: Dec. 9, 2020]
  10. N. D. Pradipta, Y. Prasetyo, and A. P. Wijaya, “Analisis pasang surut air laut menggunakan data IOC (Intergovermental Oceanographic Comission) untuk menentukan chart datum di perairan Cilacap,” Jurnal Geodesi Undip, vol. 4, no. 2, pp. 101-109, 2015
  11. J. W. Lee and S. C. Park, “Development of tsunami detection algorithm for application to Korean surge-gauge,” in the 12th Annual Meeting of Asia Oceania Geosciences Society, Suntec, Singapore, Aug. 2015
  12. J. W. Lee, S. C. Park, D. K. Lee, and J. H. Lee, ”Tsunami arrival time detection system applicable to discontinuous time series data with outliers,” Natural Hazards and Earth System Sciences, vol. 16, no. 12, pp. 2603-2622, 2016. doi: 10.5194/nhess-16-2603-2016
  13. D. G. Goring and V. I. Nikora, “Despiking acoustic doppler velocimeter data,” Journal of Hydraulic Engineering, vol. 128, no. 1, pp. 117-126, 2002. doi: 10.1061/(ASCE)0733-9429(2002)128:1(117)
  14. J. W. Lee and S. C. Park, “Development of a gap-filling algorithm applicable to a tsunami warning system,” in the 25th International Ocean and Polar Engineering Conference, Hawaii, USA, June 2015, pp. 860–864
  15. M. Rosidi, “Metode numerik menggunakan R untuk teknik lingkungan,” 2019. [Online] Available: Metode_Numerik/ . [Accessed: Sept.13, 2020]
  16. M. Hassouna, A. Tarhini, T. Elyas, and M. S. Aboutrab, “Customer churn in mobile markets: A comparison of techniques,” International Business Research, vol. 8, no. 6, pp. 224-237, 2015. doi: 10.5539/ibr.v8n6p224
  17. M. Vuk and T. Curk, ”ROC curve, lift chart and calibration plot,” Metodološki zvezki, vol. 3, no. 1, pp. 89-108, 2006. doi: 10.51936/noqf3710

Last update:

No citation recorded.

Last update: 2024-07-14 05:34:04

No citation recorded.