Tsunami Prediction Using Fuzzy Logic - IJCTA · Tsunami Prediction Using Fuzzy Logic. Twinkle Tayal...

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

mailto:[email protected]

mailto:[email protected]

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).



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.



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



597

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‟



598

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


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


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


FD as inputs



599

ISSN:2229-6093


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

.

.



600

ISSN:2229-6093

http://www.pbs.org/wgbh/nova/tsunami/

http://www.mathworks.in/help/fuzzy/building

Date post:	23-May-2018
Category:	Documents
Upload:	nguyencong
View:	223 times
Download:	1 times

Tsunami Prediction Using Fuzzy Logic - IJCTA · Tsunami Prediction Using Fuzzy Logic. Twinkle Tayal...

Documents