The Importance of Algorithmic Understanding
Concerning Tsunamis
A
Summary by Christopher
Whitfield as a part of Tsunami
5
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. 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.
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.
These
models can be developed, theoretically, based on data collected by the
DART 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. However, 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.
