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The Indian Ocean Tsunami devastated the coastline of Sri Lanka. This paper summarizes the initial efforts in understanding the tsunami wave and its hydraulic impact on the Sri Lankan coastline. It focuses attention on the hydraulic processes that led to large scale inundation, presents analysis of wave-current measurements recorded on the offshore of the Colombo Harbor and describes post-tsunami field investigations to assess the overall impact on the coastline. The paper identifies the need to model potential tsunamis and discusses issues relating to the planning of countermeasures.

The West Hellenic Arc and Trench (WHA-T) system is seismotectonically one of the most active in the European–Mediterranean region. Data on earthquake and tsunami phenomena occurring in the tectonic segment of the WHA-T from the antiquity up to the present have been updated, critically evaluated, and compiled in the standard format developed for the New European Tsunami Catalog. Most of the tsunamis documented are caused by strong earthquakes occurring in the area offshore Crete Island which is one of the most tsunamigenic areas in the entire Mediterranean Sea region. The major event of AD365 appears to be the largest ever occurred in the Mediterranean Sea at least in the last 2.5 millennia. The mean repeat time of strong tsunamis is calculated from intensity-frequency statistics and probabilistic hazard assessment is performed by assuming random temporal distribution of the events.

In the Indian Ocean and the South China Sea, many hundreds of thousands of lives have been lost due to tsunami events, and almost half of the lives lost occurred following the 2004 Indian Ocean event. Potential tsunami case scenarios have been simulated in these regions by a number of researchers to calculate the hazard level. The hazard level is based on a variety of conditions, such as the tsunami height, the inundation area, and the arrival time. However, the current assessments of the hazard levels do not focus on the tsunami risk to a coastal population. This study proposes a new method to quantify the risk to the coastal population in the region that includes the Indian Ocean and the South China Sea. The method is simple and combines the use of readily available tsunami data, far-field tsunami simulation models to determine the regional risk and global population data. An earthquake-generated tsunami was simulated, following an earthquake that had a magnitude larger than 8.5 M_w and occurred along a potential subduction zone. The 2004 Indian Ocean event seemed to be a "worst case scenario"; however, it has been estimated that a potential tsunami, occurring in a coastal region with a high population density, could cause significantly greater casualties.

As earthquake and tsunami are closely related, the probability of tsunami hazard had been done by extending the approach used in earthquakes. However, the hazard of tsunami depends also on the vulnerability of neighboring structures and hence its hazard and vulnerability should not be assessed separately. The distribution of tsunami height varies so significantly that the traditional definition parallel to that in seismic risk should be modified. Besides, previous studies on the probability of tsunami focused on the occurrence possibility of tsunami hazard in a fixed period of time, but this information is not applicable for a specific tsunami incidence. For the above-mentioned problems, a new algorithm that comprises two components is proposed in the present study. The first component, the Probabilistic Forecast of Tsunami Inundation (PFTI), is the conditional inundation probability once a tsunami of a specific height occurs, or an earthquake is detected at some specific location with a specific magnitude. PFTI comprises the assessments of both tsunami hazard and vulnerability, and can be directly applied to a specific tsunami incidence. The second component treats the Tsunamigenic Earthquake Number (TEN) modified from previous studies on tsunami hazard. These two components are combined to give the inundation possibility in a fixed period of time dubbed Earthquake-induced Tsunami Inundation Probability (ETIP) and the result can be used in urban planning or disaster mitigation guidelines. Application of this methodology to the coast of Taiwan is also discussed.

This study presents a tsunami hazard analysis for the Maldives using integrated statistical approaches, such as the WE (weight of evidence) method and a LR (logistic regression) model, using historical flooding records from the Maldives following the 2004 Indian Ocean Tsunami. The data with respect to the geological and geomorphological parameters of the islands and reefs, which were collected from 202 inhabited islands and seven resorts in the Maldives, were weighted by the presence/absence of evidence from the impacted islands. The tsunami hazard and risk were evaluated using spatial weights calculated for each variable. The predicted tsunami risk was compared with the impact of the 2004 Indian Ocean Tsunami.

The results show that for the three cases, the success rate of the estimated hazard and risk prediction ranged between 74% and 90% for the low and high impact islands, respectively. However, the predictability for medium impact islands in the three cases was within the range of 52–58%. The results of this study can be applied to hazard and risk assessments, are useful for tsunami behavior model development for coral islands and can be used to identify islands that are naturally protected, sheltered or resilient against natural disasters, such as tsunamis.

Potential tsunamis in the western Pacific Ocean pose great threats to the Chinese coastal areas. Among all possible tsunami source regions, the Manila subduction zone draws the most attention and there have been many research works on the tsunami hazards in the South China Sea. In this study, we evaluate the tsunami hazard along the Chinese coast by investigating more potential sources, including the subduction zones of Manila, Ryukyu, Nankai, Izu–Bonin and Mariana. Two tsunami scenarios are considered for each subduction zone, a worst scenario of earthquake magnitude 9.0 and a scenario of largest earthquake magnitude known in history in this zone. Earthquake source parameters are calculated using scaling relations that have been shown to be suitable for tsunami generation. Our results show that for the Chinese coast, tsunami hazards from the Manila and Ryukyu subduction zones are severe in the worst scenarios, and tsunami hazards from the Nankai, Izu–Bonin and Mariana subduction zones are mild. Using the largest earthquake magnitude in history, tsunami hazards from all the investigated subduction zones are almost negligible. Through a sensitivity test on earthquake magnitude, we find that earthquakes of magnitude of 8.5 or larger in the Manila and Ryukyu subduction zones cause severe tsunami hazard along the Chinese coast with wave amplitude over 2 m.

Probabilistic tsunami hazard and risk assessment (PTHA/PTRA) are vital tools for understanding tsunami risk and planning measures to mitigate impacts. At large-scales their use and scope are currently limited by the computational costs of numerically intensive simulations which are not always feasible without large computational resources like HPCs and may require reductions in resolution, number of scenarios modelled or use of simpler approximation schemes. To conduct the PTHA/PTRA for large proportions of a coast, we need therefore to develop concepts and algorithms for reducing the number of events simulated and more rapidly approximating the needed simulation results. This case study for a coastal region of Tohoku, Japan utilizes a limited number of tsunami simulations from submarine earthquakes along the subduction interface to generate a wave propagation and inundation database at different depths and fits these simulation results to a machine learning (ML) based variational autoencoder model to predict the intensity measure (water depth, velocity, etc.) of the tsunami at the location of interest. Such a hybrid ML-physical model can be further extended to compute the inundation for probabilistic tsunami hazard and risk onshore.