Computational Analysis of Tsunami Wave Behaviour for Three Historical Tsunami Events using T-Impulse Model

Natural catastrophes pose a serious threat to both human life and the environment because they are unpredictable. One of the most devastating natural disasters is a tsunami, and forecasting models are essential to preventing catastrophic damage to the environment and people along the coast. In the I...

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Veröffentlicht in:WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 2023-12, Vol.19, p.1357-1370
Hauptverfasser: Mohamed E., Syed, Selvan C. M., Pon
Format: Artikel
Sprache:eng
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Zusammenfassung:Natural catastrophes pose a serious threat to both human life and the environment because they are unpredictable. One of the most devastating natural disasters is a tsunami, and forecasting models are essential to preventing catastrophic damage to the environment and people along the coast. In the Impulse model, the generation of a tsunami depends on the impulse force generated during the event. Understanding tsunamis begins with simulating the tsunami generation process. This process involves simulating both the motion of the seafloor and the subsequent motion of the water above for tsunamis caused by underwater earthquakes. This modeling strategy can mimic all three stages of a tsunami: generation, propagation, and run-up. Three separate earthquake tsunami events—the 1755 Lisbon earthquake, the 1964 Alaska earthquake, the 2004 Sumatra earthquake are each investigated in this research. To demonstrate its relevance to current events and various ocean locations, the results of these events are compared and confirmed with the observed data. Analyzing the parameters used in this modeling study and identifying the parameter that has the most influence will demonstrate their significance in tsunami generation. The seabed displacement profile, seawater deformation, changes in tsunami characteristics during propagation, the tsunami’s travel time, earliest arrival time, the tsunami wave height at the coast, and inundation distance are the anticipated findings from this study. The major objective of this study is to obtain the maximum and most accurate result possible using the fewest parameters possible.
ISSN:1790-5079
2224-3496
DOI:10.37394/232015.2023.19.122