QUICK LINKS -- Team 5 Proposal -- MIT

Tsunami Prediction Algorithms

As with any examination of naturally occurring events, the number of confounding variables associated with any isolated even is almost incomprehensible; however, some sense can be made of Tsunamis as algorithmic events if the right variables are examined.

Currently, mathematical models are mainly used post-tsunami.  These models can be utilized to model the events that occurred during a given time period, generally beginning with an earthquake or other seismic event and ending with the dissipation of the tsunami waves.  For example, scientists were able to model both the 1755 and 1969 tsunami events that affected the western coasts of Europe[1].  The results of this model allowed for the determination of seismic risk factors along the coast of Portugal and Spain.

Ideally, however, mathematical models could be used to determine magnitudes and directions of tsunamis, as well as predict which area(s) along a given coast are at the highest risk during a given tsunami.  One example of a tsunami risk assessment model was developed for the coast of Japanese islands.  The first factor to consider (which remains evident in any tsunami prediction attempt) is that the arrival time and the magnitude of any tsunami will vary with the location of the fault and the specific seismic event.  Other factors considered (variable used in modeling) are as follows:  time, gravity accelerator, water level lift from still water level, water depth, friction coefficient of the ocean bottom, flux in the x and y direction, and the vertical amount of seabed displacement.  Using these factors in algorithms combining the variables allows us to determine two vital results:  estimated arrival time and the ratio of excess [2].

The arrival time of the tsunami can be determined for each section of the coast, which allows us to assess the risk for each area based on the time of arrival.  In the Japanese case, all the times were approximately twenty minutes, meaning that there exists almost no disparity between risk assessments for each area; however, if the model were applied on a larger scale (for example, on the coast of Peru), the disparities calculated for sections of the coast would allow risk factors to be determined and to allow for communications to those areas to be prioritized.

The “ratio of excess” can be determined by the total number of historical tsunamis and the arrival of tsunami waves over three meters tall at specific locations.  We can determine the probability of waves being over five meters tall in each area.  As with the arrival time, the disparities calculated based on the ratio of excess can be used to determine which areas are at greater risk [2].

The determination of risk acquired from the arrival time and the ratio of excess can also be applied to the placement of the sensor system.  If we know areas that are at a greater risk level, we can place the sensors closer to said areas so that those areas will be warned about an event more quickly than they otherwise would [2].

These models can be developed, theoretically, based on data collected by the DART II system or a similar sensor system.  In real time, data could be collected by various sensor points and instantaneously be used to estimate arrival time and, combined with a predetermined ratio of excess, to determine which areas should be evacuated or warned.

As aforementioned, the examination of natural events can be a seemingly impossible task, and by no means is it a simple one.  I have taken a step towards understanding existing models, allowing for more informed decisions concerning sensor systems for earthquakes and tsunamis to supplement current algorithms and future mathematical tsunami models.

Note:

**The reader should note that although my research assignment was obviously originally designed to be a part of Team 5’s proposal, and has benefited our proposal by providing theoretical guidelines for sensor placement, it turns out that much of my information deals more directly with Risk Assessment (Team 1) than it does with sensor development.  I have provided Team 1 with much of my information and plan to cooperate with them so that my research is not wasted but is put to good use.

Bibliography

[1] Mendes, V.L., Baptista, M.A., Miranda, J.M., Miranda, P.M.A.  (1999).  Can
Hydrodynamic Modeling of Tsunami Contribute to Seismic Risk Assessment?
Physics and Chemistry of the Earth Part A: Solid Earth and Geodesy, 24,
139-144.

[2]  Sato, Hiroaki., Murakami, Hitoshi., Kozuki, Yasunori., Yamamoto, Naoaki. (2003).
Study On Simplified Method of Tsunami Risk Assessment.  Natural Hazards, 29,
325-340.