Tsunami Impact Prediction System Based on TsunAWI Inundation Data
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2021.15.1.2Keywords:
early warning system, inundation data analysis, tsunami impact prediction system, tsunami modeling, tsunami warning informationAbstract
It is very important for tsunami early warning systems to provide inundation predictions within a short period of time. Inundation is one of the factors that directly cause destruction and damage from tsunamis. This research proposes a tsunami impact prediction system based on inundation data analysis. The inundation data used in this analysis were obtained from the tsunami modeling called TsunAWI. The inundation data analysis refers to the coastal forecast zones for each city/regency that are currently used in the Indonesia Tsunami Early Warning System (InaTEWS). The data analysis process comprises data collection, data transformation, data analysis (through GIS analysis, predictive analysis, and simple statistical analysis), and data integration, ultimately producing a pre-calculated inundation database for inundation prediction and tsunami impact prediction. As the outcome, the tsunami impact prediction system provides estimations of the flow depth and inundation distance for each city/regency incorporated into generated tsunami warning bulletins and impact predictions based on the Integrated Tsunami Intensity Scale (ITIS-2012). In addition, the system provides automatic sea level anomaly detection from tide gauge sensors by applying a tsunami detection algorithm. Finally, the contribution of this research is expected to bring enhancements to the tsunami warning products of InaTEWS.
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References
Strunz, G., Tsunami Risk Assessment in Indonesia, Natural Hazard and Earth System Science, 11, pp. 67-82, 2011.
Takagi, H., Pratama, M.B., Kurobe, S., Esteban, M., Arguiz, R. & Ke, B., Analysis of Generation and Arrival Time of Landslide Tsunami to Palu City due to the 2018 Sulawesi Earthquake, Landslide Springer-Verlag GmbH Germany, 16, pp. 983-991, 2019.
Harig, S., Androsov, A. & Rakowsky, N., Simulating Landslide Generated Tsunamis in Palu Bay, Sulawesi, Indonesia, Geophysical Research Abstracts, 21, 7094, 2019.
Omira, R., The September 28th, 2018, Tsunami in Palu-Sulawesi, Indonesia: A Post-Event Field Survey, Pure and Applied Geophysics, 176, pp. 1379-1395, 2019.
Syamsidik, S., Benazir, B., Luthfi, M. & Suppasri, A., The 22 December 2018 Mount Anak Krakatau Volcanogenic Tsunami on Sunda Strait Coasts, Indonesia: Tsunami and Damage Characteristics, Natural Hazard and Earth System Science, 20, pp. 549-565, 2019.
Borrero, J.C., Surveys, P.F. & June, M., Field Survey of Northern Sumatra and Banda Aceh, Indonesia after the Tsunami and Earthquake of 26 December 2004, Seismological Research Letter, 76, pp. 312-320, 2005.
Mch, U., Rudloff, A. & Lauterjung, J., Postface ?The GITEWS Project ? Results, Summary and Outlook?, Natural Hazards and Earth System Science, 11(3), pp. 765-769, 2011.
Harig, S., 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, 177, pp. 1379-1401, 2019.
Goto, C., Ogawa, Y., Shuto, N. & Imamura, F., Numerical Method of Tsunami Simulation with the Leap-frog Scheme, IOC Manuals and Guides No. 35, UNESCO Paris France., 1997.
Imamura, F., Yalciner, A.C. & Ozyurt, G., Tsunami Modelling Manual, first version June 1995, last revision April 2006. [Online]. Available: http://www.tsunami.civil.tohoku.ac.jp/hokusai3/J/projects/manual-ver-3.1.pdf. (Accessed April 2020).
Rakowsky, N., Operational Tsunami Modelling with Tsunawi ? Recent Developments and Applications, Natural Hazard and Earth System Science, 13, pp. 1629-1642, 2013.
Immerz, A., Harig, S. & Rakowsky, N., Extending and Visualizing the TsunAWI Simulation Database of the Indonesia Tsunami Early Warning System (InaTEWS), Krause, G. (Ed.) Building Bridges at the Science-Stakeholder Interfac,. SpringerBriefs in Earth System Sciences. Springer, Cham, pp. 101-107, 2018.
Badan Meteorologi Klimatologi dan Geofisika, Tsunami Early Warning Service Guidebook for InaTEWS-Second Edition, Badan Meteorologi Klimatologi dan Geofisika, 2012.
Lekkas, E.L., Andreadakis, E., Kostaki, I. & Kapourani, E., A Proposal for a New Integrated Tsunami Intensity Scale (ITIS-2012), Bulletin Seismological Society of America, 103(2B), pp. 1493-1502, 2013.
Grthal, G., European Macroseismic Scale 1998, 15. Conseil de l?Europe, 1998.
Michetti, A.M., Environmental Seismic Intensity Scale ? ESI 2007, in Memorie Descrittive della Carta Geologica d?Italia, 74, L. Guerrieri and E. Vittori, Eds. Servizio Geologico d?Italia-Dipartimento Difesa del Suolo, APAT, Roma, Italy, pp. 7-54, 2007.
Musa, A., Real-Time Tsunami Inundation Forecast System For Tsunami Disaster Prevention And Mitigation, Journal of Supercomputing, 74(7), pp. 3093-3113, 2018.
Gusman, A.R., Tanioka, Y., MacInnes, B.T. & Tsushima, H., A Methodology for Near-Field Tsunami Inundation Forecasting: Application to the 2011 Tohoku Tsunami, Journal of Geophysical Research: Solid Earth, 119, pp. 8186-8206, 2014.
Jaffe, B.E., Northwest Sumatra and Offshore Islands Field Survey after the December 2004 Indian Ocean Tsunami, Earthquake Spectra, 22(3), pp. S105-S135, 2006.
Harig, S., Chaeroni, Pranowo, W.S. & Behrens, J., Tsunami Simulations on Several Scales-Comparison of Approaches with Unstructured Meshes and Nested Grids, Ocean Dynamics, 58, pp. 429-440, 2008.
Dermadi, Y. & Bandung, Y., Analysis of Numerical Model Result To Estimate Tsunami Damage Based on Inundation Data, International Symposium on Electronics and Smart Devices (ISESD), 2019.
Mofjeld, H.O., The Tsunami Detection Algorithm, Not published paper. [Available at http://nctr.pmel.noaa.gov/tda_documentation.html], 1997.
Golchha, N., Big Data ? The Information Revolution, International Journal of Applied Research, 1(12), pp. 791-794, 2015.
Islam, M., Data Analysis: Types, Process, Methods, Techniques and Tools, International Journal on Data Science and Technology, 6(1), pp. 10-15, 2020.
Iliou, T., Konstantopoulou, G., Stephanakis, I., Anastasopoulos, K., Lymberopoulos, D. & Anastassopoulos, G., Iliou Machine Learning Data Preprocessing Method for Stress Level Prediction, IFIP Advances in Information and Communication Technology, 519, pp. 351-361, 2018.
Marjani, M., Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges, IEEE Access, 5, pp. 5247-5261, 2017.
Al-Neama, M.W., Reda, N.M. & Ghaleb, F.F.M., An Improved Distance Matrix Computation Algorithm for Multicore Clusters, Journal of Biomedicine and Biotechnology, 2014, pp. 1-12, 2014.
Wessel, P. & Smith, W.H.F., A Global, Self-consistent, Hierarchical, High-Resolution Shoreline Database, Journal of Geophysical Research, 101(96), pp. 8741-8743, 1996.
Koshimura, S., Oie, T., Yanagisawa, H. & Imamura, F., Developing Fragility Functions for Tsunami Damage Estimation Using Numerical Model and Post-Tsunami Data from Banda Aceh, Indonesia, Coastal Engineering Journal, 51(3), pp. 243-273, 2009.
Puschner, P. & Koza, C., Calculating the Maximum Execution Time of Real-time Programs, Real-Time Systems, 1(2), pp. 159-176, 1989.
La Fraga, L.G.D. Tlelo-Cuautle, E. & Azucena, A.D.P., On the Execution Time of a Computational Intensive Application in Scripting Languages, International Conference in Software Engineering Research and Innovation (CONISOFT), 2018(1), pp. 149-152, 2018.
