Tsunami Database Development in the Sunda Arc Indonesia to Support Early Warning through Artificial Intelligence Technology

Mardi Wibowo, Wahyu Hendriyono, Hanah Khoirunnisa, Reno Arief Rachman, Widjo Kongko, Gugum Gumbira, Shofia Karima, Reni Wijayanti, Eko Kustiyanto, Amalia Nurwijayanti, Destianingrum Ratna Prabawardani, Gumilang Ramadhan Pasma


The Sunda Arc-Indonesia is very vulnerable to tsunamis. There have been at least 55 tsunamis from 416–2018. Tsunami in the Sunda Arc is classified as a near-field tsunami with an arrival time of < 30 minutes after the earthquake. Meanwhile, the BMKG issued a warning within 5 minutes after the earthquake; therefore, speed in giving warnings is very vital. Artificial intelligence is an alternative technology that can quickly predict a tsunami's height and arrival time. For developing this technology, adequate quality and quantity of data and information on tsunamis are needed. Therefore, this study was conducted to build a tsunami database based on the results of simulations and numerical modeling of multiple scenarios from hypothetical and historical earthquake sources. This study used the open-source TUNAMI F1 model. This model simulates the propagation of tsunami waves using a linear equation. This study obtained 465 hypothetical earthquake sources, 534 historical earthquake sources, and 9,990 datasets from tsunami model simulation results. Each dataset contains ten information. Based on the 8.2 magnitude earthquake scenario, the potential tsunami hazard is 3–47 m with an estimated arrival time of < 30 minutes. An earthquake < 7 Mw can trigger a tsunami, especially an earthquake that is shallow and close to the coast, even though the tsunami height is < 0.5m. This data will be used to train an artificial intelligence-based tsunami prediction system. The artificial intelligence-based tsunami prediction system is expected to be used to strengthen the Indonesia tsunami early warning system (InaTEWS).


Artificial intelligence; InaTEWS; near-field tsunami; Sunda Arc; tsunami database; tsunami modeling

Full Text:



H. Harjono et al., Seismotektonik busur Sunda, 1st ed. Jakarta, Indonesia: LIP Press, 2017.

M. Kirchenbaur et al., “Sub-arc mantle enrichment in the Sunda rear-arc inferred from HFSE systematics in high-K lavas from Java,†Contributions to Mineralogy and Petrology, vol. 177, no. 1, pp. 1–25, 2022.

R. Hall, M. W. A. van Hattum, and W. Spakman, “Impact of India-Asia collision on SE Asia: The record in Borneo,†Tectonophysics, vol. 451, no. 1–4, pp. 366–389, Apr. 2008.

R. Triyono et al., Katalog Tsunami Indonesia Per-Wilayah Tahun 416-2018, 2nd ed. Jakarta, Indonesia: Agency for Meteorology, Climatology and Geophysics (BMKG), 2019.

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 and Applied Geophysics, vol. 177, no. 3, pp. 1379–1401, 2020.

A. Paris, P. Heinrich, R. Paris, C. Guerin, H. Hebert, and A. Gailler, “Numerical modeling of the December 22, 2018 Anak Krakatau landslide and the following tsunami in Sunda Strait, Indonesia,†in IEEE, 2019, pp. 1–6.

J. C. Borrero et al., “Field Survey and Numerical Modelling of the December 22, 2018 Anak Krakatau Tsunami,†Pure and Applied Geophysics, vol. 177, no. 6, pp. 2457–2475, Jun. 2020.

WinITDB, “Integrated Tsunami Database for the World Ocean,†2020. [Online]. Available: http://tsun.sscc.ru/WinITDB.htm. [Accessed: 01-Jul-2020].

J. A. Reid and W. D. Mooney, “Tsunami Occurrence 1900–2020: A Global Review, with Examples from Indonesia,†Pure and Applied Geophysics, vol. May, p. 23, 2022.

J. Goff, “Tsunami databases,†Geological Records of Tsunamis and Other Extreme Waves, pp. 75–93, Jan. 2020.

C. Liu et al., “Numerical Investigation of Sediment Transport of Sandy Beaches by a Tsunami-Like Solitary Wave Based on Navier-Stokes Equations,†Journal of Offshore Mechanics and Arctic Engineering, vol. 141, no. 6, Dec. 2019.

T. Nacházel et al., “Tsunami-related data: A review of available repositories used in scientific literature,†Water (Switzerland), vol. 13, no. 16, pp. 1–31, 2021.

S. Widiyantoro et al., “Implications for megathrust earthquakes and tsunamis from seismic gaps south of Java Indonesia,†Scientific Reports, vol. 10, no. 1, pp. 1–11, Dec. 2020.

Presiden Republik Indonesia, Peraturan Presiden No. 93 Tahun 2019 Tentang Penguatan dan Pengembangan Sistem Informai Gempa Bumi dan Peringatan Dini Tsunami (Presidential Regulation of the Republic of Indonesia No. 93 of 2019 Concerning the Strengthening and Development of Earthquake. Indonesia: Lembaran Negera Republik Indonesia Tahun 2019 Nomor 266, 2019, p. 19.

D. Inazu, N. Pulido, E. Fukuyama, T. Saito, J. Senda, and H. Kumagai, “Near-field tsunami forecast system based on near real-time seismic moment tensor estimation in the regions of Indonesia, the Philippines, and Chile 4. Seismology,†Earth, Planets and Space, vol. 68, no. 1, Dec. 2016.

B. P. Kumar et al., “Tsunami Early Warning System-an Indian Ocean Perspective,†Journal of Earthquake and Tsunami, vol. 2, no. 3, pp. 197–226, Nov. 2008.

I. Mudita, W. Hendriyono, G. E. Putri, M. Wibowo, G. Gumbira, and N. D. Sulistyodarmayanti, “Preliminary Research in Tsunami Modelling-Leveraging Artificial Intelligence Technology,†in Proceeding - 2021 IEEE Ocean Engineering Technology and Innovation Conference: Ocean Observation, Technology and Innovation in Support of Ocean Decade of Science, OETIC 2021, 2021.

M. Romano et al., “Artificial neural network for tsunami forecasting,†Journal of Asian Earth Sciences, vol. 36, no. 1, pp. 29–37, 2009.

S. Marras and K. T. Mandli, “Modeling and simulation of tsunami impact: A short review of recent advances and future challenges,†Geosciences (Switzerland), vol. 11, no. 1, pp. 1–19, 2021.

H. Mase, T. Yasuda, and N. Mori, “Real-Time Prediction of Tsunami Magnitudes in Osaka Bay, Japan, Using an Artificial Neural Network,†Journal of Waterway, Port, Coastal, and Ocean Engineering, vol. 137, no. 5, pp. 263–268, Sep. 2011.

J. F. Rodríguez, J. Macías, M. J. Castro, M. de la Asunción, and C. Sánchez-Linares, “Use of Neural Networks for Tsunami Maximum Height and Arrival Time Predictions,†GeoHazards, vol. 3, no. 2, pp. 323–344, 2022.

I. E. Mulia, A. R. Gusman, and K. Satake, “Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes,†Journal of Geophysical Research: Solid Earth, vol. 125, no. 9, p. e2020JB019690, Sep. 2020.

F. Makinoshima, Y. Oishi, T. Yamazaki, T. Furumura, and F. Imamura, “Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks,†Nature Communications, vol. 12, no. 1, pp. 1–10, 2021.

E. Irfiani, A. Pius, P. Raben, V. S. Hadi, and M. Iqbal, “Classification of Tsunami Potential Based on Earthquakes in Indonesia Using the C4 . 5 Algorithm,†Jurnal Mantik, vol. 6, no. 3, pp. 3730–3736, 2022.

M. J. Song and Y. S. Cho, “Modeling maximum tsunami heights using bayesian neural networks,†Atmosphere, vol. 11, no. 11, pp. 1–13, 2020.

J. H. Lin, Y. F. Chen, C. C. Liu, and G. Y. Chen, “Building a pre-calculated quick forecast system for tsunami run-up height,†Journal of Earthquake and Tsunami, vol. 8, no. 3, p. 1440002, 2014.

U. Setiyono, A. R. Gusman, K. Satake, and Y. Fujii, “Pre-computed tsunami inundation database and forecast simulation in Pelabuhan Ratu, Indonesia,†Pure and Applied Geophysics, vol. 174, no. 8, pp. 3219–3235, Aug. 2017.

I. Ibtihaj, S. Suparno, M. Rizqy Septyandy, G. Abdul Jabbar, and T. Rani Puji Astuti, “Indonesia paleotsunami database as an effort to reduce the tsunami disasters in Indonesia,†in E3S Web of Conferences 13th AIWEST-DR 2021 - Aceh International Workshop and Expo on Sustainable Tsunami Disaster Recovery - Post-the 2004 Indian Ocean Tsunami Challenges: Strengthening Community Resilience from Compound Disasters in Pandemic Situations, 2022, vol. 340, p. 01001.

T. Handayani, R. M, S. Harig, A. Immerz, N. Rakowsky, and J. Grifin, “Extending the database of pre-computed tsunami simulations for the Indonesian tsunami early warning system (InaTEWS) | EPIC,†in International Tsunami Symposium Bali-Flores 21-25 August 2017, 2017, p. 13.

D. H. F. Pradana, E. A. Wiguna, R. Amien, A. N. Saputri, M. Wibowo, and W. Hendriyono, “Development of Real-Time Tsunami Early Warning System Dashboard Based on Tunami-F1 and Machine Learning in Sunda Arc , Indonesia,†in The 2022 IEEE Ocean Engineering Technology and Innovation Conference: Management and Conservation for Sustainable and Resilient Marine and Coastal Resources, 2022, p. 7.

Daryono, “Peringatan Dini Tsunami di Selatan Jawa.†BMKG & Badan Geologi, Bandung, p. 66, 2020.

Indonesian Agency for Geospatial Information (BIG), “Peta BATNAS dan DEMNAS,†Jakarta, 2018.

F. Imamura, A. C. Yalçiner, and G. Ozyurt, Tsunami Modelling Manual, 2nd ed., no. April. Sendai Japan, 2006.

S. H. Rahmawan, G. Ibrahim, M. A. Mustofa, and M. Ahmad, “Studi Potensi Bahaya Tsunami di Selatan Jawa,†Bandung, 2012.

M. Wibowo, “Modeling the Potential of Tsunami Hazard in Labuan Bajo Towards A Disaster-Resilient Tourism Area,†Indonesian Journal of Geography, vol. 54, no. 1, 2022.

N. H. Mardi, M. A. Malek, M. S. Liew, and H. E. Lee, “A Conceptual Review of Tsunami Models Based on Sumatera-Andaman Tsunami Event,†in InCIEC 2014, 2015, pp. 387–396.

B. Adriano, Y. Fujii, and S. Koshimura, “Tsunami source and inundation features around Sendai Coast, Japan, due to the November 22, 2016 M w 6.9 Fukushima earthquake,†Geoscience Letters, vol. 5, no. 1, p. 12, Dec. 2018.

G. P. Hayes et al., “Slab2, A Comprehensive Subduction Zone Geometry Model,†Science, vol. 362, no. October, pp. 58–61, 2018.

A. M. Dziewonski, T. A. Chou, and J. H. Woodhouse, “Determination of earthquake source parameters from waveform data for studies of global and regional seismicity.,†Journal of Geophysical Research, vol. 86, no. B4, pp. 2825–2852, 1981.

G. Ekström, M. Nettles, and A. M. Dziewoński, “The global CMT project 2004-2010: Centroid-moment tensors for 13,017 earthquakes,†Physics of the Earth and Planetary Interiors, vol. 200–201, pp. 1–9, Jun. 2012.

A. Rahman, A. N. Vita, and S. Rohadi, “Seismic Gap Identification along Sunda Trench using Mapping Analysis of Energy Accumulation Zone,†in AIP Conference Proceedings, 2021, vol. 2320, no. March, pp. 5–10.

W. Kongko and T. Schlurmann, “The Java tsunami model: Using highly-resolved data to model the past event and to estimate the future hazard,†in Proceedings of the Coastal Engineering Conference, 2011, pp. 1–16.

L. Mansinha and D. E. Smylie, “The Displacement Fields of Inclined Faults,†Bulletin of the Seismological Society of America, vol. 61, no. 5, pp. 1433–1440, Oct. 1971.

F. Imamura, “Review of tsunami simulation with a finite difference method.†World Scientific, pp. 25–42, 1996.

Research Center for Hydrodynamic Technology, “Final Report Tsunami Modeling and Data Analyze for Read Down Station,†Yogyakarta, 2021.

G. P. Hayes, “USGS Subduction Zone Geometry (Slab 1.0 & Slab 2) and their use in Probabilistic Seismic Hazard Analysis,†2018.

D. L. Wells and K. J. Coppersmith, “New Empirical Relationships among Magnitude, Rupture Length, Rupture Width, Rupture Area, and Surface Displacement,†1994.

A. Cipta and F. Imamura, “Study on tsunami numerical modeling for making tsunami hazard maps in indonesia,†Bulletin of the International Institute of Seismology and Earthquake Engineering, vol. 43, no. May 2017, pp. 127–132, 2009.

R. R. R. Alam, M. B. Adityawan, M. Farid, A. Chrysanti, Widyaningtias, and M. A. Kusuma, “Tsunami-induced inundation on the coast of Palu City,†in IOP Conference Series: Earth and Environmental Science, 2021, vol. 708, no. 1.

G. Pasau, G. H. Tamuntuan, and A. Tanauma, “Numerical modelling for tsunami wave propagation (case study: Manado bays),†in IOP Conference Series: Materials Science and Engineering, 2019, vol. 567, no. 1.

T. M. Rasyif, S. Kato, Syamsidik, and T. Okabe, “Numerical simulation of morphological changes due to the 2004 tsunami wave around Banda Aceh, Indonesia,†Geosciences (Switzerland), vol. 9, no. 3, 2019.

L. H, P. N. T, and I. F, “Tsunami catalog and zones in indonesia,†J. of Natural Disaster Science, vol. 22, p. 25, 2000.

DOI: http://dx.doi.org/10.18517/ijaseit.13.5.18616


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development