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Tsunami Prediction Using Fuzzy Logic
Twinkle Tayal 1, Dr. Prema KV
2
1 M.Tech, 2nd year,CSE,FET, MUST, RAJASTHAN 2 HOD, CSE, FET, MUST,RAJASTHAN
[email protected] [email protected]
Abstract
One of the most terrifying natural hazards known to man that has been liable for mammoth loss of life and
property throughout history is tsunami. Tsunamis
have large and prominent effect on the human, social
and economic sectors of our societies because of
their destructive nature. There are number of
methods and algorithms that are used to minimize the
destruction by detecting tsunami and warn people
before hand. In this paper we are proposing one such
method. We are proposing an alert system that will
notify whether tsunami is rare, advisory or definite
based on the different parameters. The system is
designed using Matlab fuzzy logic toolbox. All the
data used in the work is real- time and taken from
NOAA’s tsunami historical database.
1. Introduction Tsunami is originally a Japanese word with the
English meaning, “harbor wave”. It is epitomized by
two characters, first is, "tsu," which means harbor,
and the second one is "nami" which means “wave”.
Tsunami can also be called as “seismic sea waves” by
the scientific community and “tidal waves” by local
public. A tsunami is a very long-wavelength wave of
water that is formed by the hasty disarticulation of
the seafloor or interruption in the standing water
.Tsunami waves are different from those normal sea
waves, as the wavelength of tsunami is far longer.
Primarily, they bear a resemblance to a rapidly rising
tide, and for this reason they are often called as tidal
waves [1]. Although the effect of tsunamis is limited
to coastal areas, destruction caused by them can be
gargantuan and they can even affect the entire ocean basins. Along with the wretched human loss of life
and impairment to habitations and infrastructure, the
environment can be ruined by the impact of the
access of salt water into the agricultural lands by a
major tsunami event. The vegetation also got
extremely badly influenced by the physical force of
the waves. It would be right to say that the effect of a
major tsunami on the environment is gigantic [2].
There are number of researches going on to predict
this natural hazard, so that, people can be warned
beforehand. The destruction caused by this
natural hazard can‟t be minimized, but, if people
warned earlier, many lives can be saved. There are
many methods and algorithms which are being used
to predict the tsunami. On such method that can be
used in this field is “fuzzy logic”. The undeniable
reason for selection of fuzzy logic model in this work is the natural fuzziness and vagueness in the nature of
tsunami and the difference of influence of different
parameters of tsunami.
2. Fuzzy Logic L.A. Zadeh set the basis of fuzzy set theory as a
process to deal with the haziness of practical systems
in 1965[3]. Bellman and Zadeh wrote that “Much of
the decision making in the real world takes place in
an environment in which the goals, the constraints and the consequences of possible actions are not
known precisely” [4]. This vagueness is the mainstay
of fuzzy set. Fuzzy sets were anticipated as a
simplification of classical set theory. The application
of fuzzy logic is becoming a domineering tool in
addressing the issues of environmental science and
policy[5]. It‟s becoming a prevalent practice to
constantly deal with the linguistic terms. Intuition is a
fuzzy method that requests no introduction. It comes
from the human ability of sprouting membership
functions on the basis of their own understanding.
Fuzzy logic accomplishments human thinking and
reasoning and relate the model to problems according
to needs. It endeavours to equip computers with the
proficiency to process special data of humans and to
work via their experiences and understandings. When
human logic untangles problems, it creates verbal rules like “if <event realized> is this, then <result> is
that". Fuzzy logic makes an attempt to accustom
these verbal rules and the aptitude to make decisions
of humans to machines/computers. It exploits verbal
variables and terms together with verbal rules. Verbal
rules and terms usually used in human decision-
making process are fuzzy or hazy rather than exact,
accurate or precise. A fuzzy decision-making
procedure makes use of symbolic verbal phrases as
an alternative of numeric values. Relocating these
symbolic verbal phrases to computers is based on
mathematics. The foundation of this mathematical is
Twinle Tayal et al, Int.J.Computer Technology & Applications,Vol 5 (2),594-600
IJCTA | March-April 2014 Available [email protected]
594
ISSN:2229-6093
fuzzy logic. Systems that use fuzzy logic are surrogates to the intricacy of mathematical modelling
of difficult non-linear problems and fuzzy logic
meets mathematical modelling requirement of a
system. Systems that use fuzzy logic can give
valuable results based on indistinguishable verbal
knowledge like humans. In fuzzy logic, information
is in form of verbal phrases or we can say linguistic
terms such as big, small, very, few etc rather than
numeric values. If behaviour of a system can be
uttered by means of rules or requires very complex
non-linear processes, then fuzzy logic approach can
be applied in that system [6].
2.1.1 Mamdani’s Fuzzy Inference Method Mamdani's method was one of the very first control
systems that were built by means of fuzzy set theory.
In 1975, it was proposed by Ebrahim Mamdani as an
endeavor to control an amalgamation of steam engine
and boiler by synthesizing a set of linguistic control
rules attained from experience of human operators.
Mamdani's effort was established on Lotfi Zadeh's 1973 paper on fuzzy algorithms for complex systems
and decision processes. Mamdani-type inference
foresees the output membership functions to be fuzzy
sets. There is a fuzzy set for each output variable that
needs defuzzification after the aggregation process. It
is feasible and moreover proficient in many cases to
make use of a single spike as the output membership
functions rather than a distributed fuzzy set. This is
sometimes referred to as a singleton output
membership function. It raises the effectiveness of
the defuzzification process as it utterly simplifies the
computation required by the more general Mamdani
method, which ascertains the centroid of a two-
dimensional function. It can be build by using either
command line functions or with the graphical user
interface (GUI) present in the Matlab. In the present
work, tsunami prediction is being done by building a Mamdani fuzzy inference system using the GUI tools
in the Matlab fuzzy logic toolbox , which basically
consists of five editors which can be used to build,
edit and view the system, as shown in figure 1,
namely
Fuzzy Inference System (FIS) Editor –this is the first
editor that comes across in the procedure. It deals
with the some of the high-level issues for the system
like the number of input and output variables and
their names.
Membership Function Editor- this editor is basically
used to label the shapes and characteristics of all the
membership functions allied with each variable.
Rule Editor- forms the basis of the fuzzy inference
system. It is utilized to edit the list of rules that
classifies the behavior of the system. One can add,
delete or make changes in the rules any time by using this editor.
Rule Viewer- it is a strictly read-only tool. It is
basically employed to view the fuzzy inference
diagram. This viewer can be used as an analytic to
see which rules are active on the analogous input you
have entered and how the individual membership
function shapes convince the results.
Surface Viewer – this is also a read-only editor which
can be employed to view the dependence of one of
the outputs on any one or two of the inputs. It is
utilized to obtain and plot an output surface map for
the system [7].
Figure 1.GUI editors in Mamdani fuzzy method
3. Tsunami Generation Most of the oceanic tsunamis (up to 75% of all
historical cases) are engendered by the shallow-focus
earthquakes adept of transferring adequate energy to
the overlying water column, as shown in figure2.
The rest is alienated among the landslide (7%),
volcanic (5%) and meteorological (2%) tsunamis.
Up to 10% of all the accounted historical run-ups still
have unrevealed sources. Some of the recent studies (like, Gusiakov, 2003) shown that actually the
portion of tsunamigenic event where slide-generation
mechanism was cosseted can be much higher (up to
30% of all cases).
Twinle Tayal et al, Int.J.Computer Technology & Applications,Vol 5 (2),594-600
IJCTA | March-April 2014 Available [email protected]
595
ISSN:2229-6093
Figure 2. Causes of Tsunami[8]
The fundamental and foremost cause of a
tsunami is the dislodgment of a substantial volume of
water or perturbation of the sea. This disarticulation
of water is generally attributed to earthquakes,
landslides, volcanic eruptions, and glacier calvings or
more occasionally by the meteorites and nuclear
tests. The waves formed in this way are then
prolonged by gravity. Tides do not play any role in
the generation of tsunamis. Most of these tsunamis are engendered by
earthquakes that cause disarticulation of the seafloor,
but, tsunami can be generated by volcanic eruptions,
landslides, underwater explosions, and meteorite
impacts too. Along with these causes, there are many
other parameters that can be taken into account like
the focal depth of earthquake, period, wavelength,
displacement of water, epicentral distance, and
expected run-up of the water and so on. So, based on
some of these parameters, this work will predict the
occurrence of tsunami and the type of alert [8].
3.1 Parameters used Essentially, as according to the geologists the
different causes of tsunami can‟t be put together into
a specific range for whole world. The tsunami event
elicits as according to the place and the according to
the different environmental conditions. But based on
the global tsunami historical data of tsunami by
NOAA (NGDC/WDS) [9] and the other
organizations, the data ranges of the different parameters can be appraised. In the present work, the
ranges are defined by studying the actual existing
historical database and the information of tsunami
provided by the organizations like NOAA pacific
tsunami warning centre, Japan meteorological
agency, UNESCO international tsunami information
centre, Wikipedia, Indian tsunami agency. All these parameters will be referred as inputs in the fuzzy
logic system that will be designed by the help of
fuzzy logic toolbox of Matlab. These parameters can
be defined as follows-
1. Earthquake- it is a series of vibrations or
sensations or movements in the crust of the
earth. An earthquake that arises besides the
coastlines or anywhere beneath the oceans can
cause tsunami. The size of the tsunami
generally depends on the size of the
earthquake, with larger earthquakes causes‟
larger tsunami. It is measured in Richter scale.
2. Landslides- Landslides that moves into
oceans, bays, or lakes can also cause tsunami.
Most such landslides are caused by
earthquakes or volcanic eruptions.
3. Volcanic eruption- Volcanoes that happen
along coastal zones can cause several effects that can become a reason of a tsunami.
Explosive and volatile eruptions can promptly
emplace pyroclastic flows into the water,
landslides and debris avalanches caused by
eruptions can swiftly move into the water, and
collapse of volcanoes to form calderas can
suddenly dislocate the water. The extent of
explosion is generally measured in VEI
(Volcanic Explosivity Index) which varies
from 0 to 8 [10].
4. Focal depth (FD) - It is the depth of an
earthquake hypocenter (the point within the
earth where an earthquake fissure starts). The
occurrence of tsunami when caused by the
earthquake depends on the focal depth. The
shallow focal earthquakes are most destructive.
In this work we have taken 0 to 65 km as optimal range for shallow focal depth [1].
5. Water vertical displacement (VD) –
whatever be the cause of tsunami, a tsunami is
disparaging only when the amount of water is
displaced vertically. So, we have considered it
as an input for our system.
4. Implementation The Fuzzy logic tsunami warning system model can
be shown by help of Matlab Fuzzy logic toolbox editors as shown in the figures below.
Twinle Tayal et al, Int.J.Computer Technology & Applications,Vol 5 (2),594-600
IJCTA | March-April 2014 Available [email protected]
596
ISSN:2229-6093
Figure 3.Fuzzy inference system for the model
4.1 Membership Values Membership values are defined by referencing and
studying the real-time data from the NOAA tsunami
historical database as shown in table 1.
TABLE 1.Membership values PARAMETER VALUES
EQ WEAK- 0 to 4.5
MILD – 4.5 to 7.5
STRONG- 7.5 to 10
VEI • NON_EXPLOSIVE- 0 to
2
• MILD- 2 to 4
• EXPLOSIVE- 4 to 8
LS WEAK- 0 to 3.5
MILD – 3.5 to 7
• STRONG- 7 to 10
FD • SHALLOW- 0 to 65
• MODERATE- 65 to 300
• DEEP- 300 to 700
VD • LESS- 0 to 4
• MODERATE- 4 to 6.5
• LARGE- 6.5 to 10
ALERT(OUTPUT) • RARE- 0 to 0.35
• ADVISORY-0.35 to 0.65
• WARNING- 0.65 to 1
4.2 Membership Function Editors
Different membership function editors for the inputs and output are shown in the figures below.
Figure 4.MF editor for EQ
Figure 5 MF editor for VEI
Figure 6.MF editor for LS
Figure 7.MF editor for FD
Twinle Tayal et al, Int.J.Computer Technology & Applications,Vol 5 (2),594-600
IJCTA | March-April 2014 Available [email protected]
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ISSN:2229-6093
Figure 8.MF editor for VD
Figure 9.MF editor for ALERT (output)
The rule editor that is used for entering and editing
the rules in the system, which defines the behaviour
of the system, is shown in the figure 10.
Figure 10.Rule editor
5. Results The rule viewer and surface viewer editors are used
for receiving the results. In this work, when we take
different values for the different parameters, after
defuzzifying the individual outputs, rule viewer
shows a crisp output, which is the real output. There
are five defuzzification methods available in the
Matlab fuzzy logic toolbox. The defuzzification method used by us in the current work is centroid
method, as it is the most accurate and commonly
used defuzzification method. The surface viewer in
essence works sound for 2 inputs, but if there is large
no. of inputs, any two inputs can be taken which are
supposed to be as constant. The results are shown in figures below.
For example, as input we take following values [3,
0,0,350,2] for different parameters as explained in
table 2, then, according to our understanding of the
input parameters, output should be that tsunami alert
will be RARE. On feeding these values in our
generated system, we are getting result as 0.158,
which is coming under range of „rare‟.
TABLE 2.Input values when decisive parameters are in
rare range PARAMETER VALUE TAKEN ALERT
EQ 3 RARE
VEI 0 RARE
LS 0 RARE
FD 350 RARE
VD 2 RARE
Figure 11.Rule viewer for above input
In the same way, on feeding values of different
parameters in different ranges, like when in advisory range or when in warning range, the results that came
out are shown in tables and figures below.
On feeding values [8 0 0 350 4] in our generated
system, we are getting result as 0.469, which is
coming under range of „advisory‟
Twinle Tayal et al, Int.J.Computer Technology & Applications,Vol 5 (2),594-600
IJCTA | March-April 2014 Available [email protected]
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ISSN:2229-6093
TABLE 3.Input values when decisive parameters are in advisory range
PARAMETER VALUE TAKEN ALERT
EQ 8 ADVISORY
VEI 0 ADVISORY
LS 0 ADVISORY
FD 350 ADVISORY
VD 4 ADVISORY
Figure 12.Rule viewer for above input
TABLE 4.Input values when decisive parameters are in
warning range PARAMETER VALUE TAKEN ALERT
EQ 8 WARNING
VEI 0 WARNING
LS 0 WARNING
FD 40 WARNING
VD 8 WARNING
Figure 13.Rule viewer for above input
On feeding values [8 0 0 40 8] in our generated
system, we are getting result as 0.819, which is
coming under range of „warning‟
As different surface views are possible on taking any
two inputs as x and y axis, some of the generated
surface views on taking different inputs as x and y
axis are shown in the figures below.
Figure 13.surface viewer for the system with EQ and
VEI as inputs
Figure 14.surface viewer for the system with EQ and
FD as inputs
Twinle Tayal et al, Int.J.Computer Technology & Applications,Vol 5 (2),594-600
IJCTA | March-April 2014 Available [email protected]
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ISSN:2229-6093
Figure 15.surface viewer for the system with EQ and
VD as inputs
6. Conclusions Fuzzy logic bestows a deputy to epitomize linguistic
and subjective traits of the real world in computing.
The reason for selecting the fuzzy logic model in this
work is that system uses fuzzy logic model divulges
effective and real results based on the uncertain,
vague, indecisive, hazy and imprecise verbal
knowledge same as the logic of a human being.
Moreover, it takes long time to solve such problems by using other present methods and we can reach a
wide-ranging solution by doing limited number of
experiments by using fuzzy. Mamdani fuzzy
inference system has been designed in this work. The
Method has been found capable of predicting the
alert type of the tsunami based on the several
parameters and conditions, which we have
generalized from the real time situations from the
information provided by the historical database by
NOAA. The obtained results emerge to be a realistic
and reasonable façade with the desired results. The
prediction scheme presented here can be considered
as a step towards the tsunami prediction, which can
successfully be applied by taking other parameters
into consideration and data of any particular specific
area.
10. References [1] http://en.wikipedia.org/wiki/Tsunami
[2] http://www.pbs.org/wgbh/nova/tsunami/
[3] L.A. Zadeh, “Fuzzy Sets,” in Information and Control, vol. 8. New York: Academic Press,
1965, pp. 338-353.
[4] R. E. Bellman and L. A. Zadeh, “Decision-
making a fuzzy environment,” Management Science, vol. 17, pp. 141-164,1970.
[5] K. Tomsovic and M.Y.Chow,” Tutorial on Fuzzy
Logic Applications in Power Systems” IEEE-
PES winter meeting, Singapore, 2000.
[6] Poongodi, M., Manjula, L., Pradeepkumar, S. and Umadevi, M.(Dec 2011), Cancer prediction
technique using fuzzy logic, International journal
of Current Research, Vol. 3, Issue 11, pp. 333-
336.
[7] http://www.mathworks.in/help/fuzzy/building systems-with-fuzzy-logic-toolbox-
software.html#FP6300
[8] http://tsun.sscc.ru/tsun_hp.htm
[9] National Geophysical Data Center / World Data
Service (NGDC/WDS): Global Historical Tsunami Database. National Geophysical Data
Center, NOAA. doi:10.7289/V5PN93H7
[10] http://academic.evergreen.edu/g/grossmaz/springl
e
.
.
Twinle Tayal et al, Int.J.Computer Technology & Applications,Vol 5 (2),594-600
IJCTA | March-April 2014 Available [email protected]
600
ISSN:2229-6093