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Journal of Engineering Structures (JOES)
Volume 6, Issue 7, Jul 2015, pp. 44-52, Article ID: JOES_06_07_006
Available online at http://www.iaeme.com/JOES/issues.asp?JType=JOES&VType=6&IType=7
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication
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B. J. Agarwal
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Department of Textile Chemistry
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Faculty of Technology and Engineering
The Maharaja Sayajirao University of Baroda, Vadodara
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Cite this Article: Omran, Z. A. and Jaber, W. S. Analysis the Deficit of
Dissolved Oxygen in Al_Hilla River According to Wastes Disposal and
Velocity of Stream. Journal of Engineering Structures , 6(7), 2015, pp. 44-52.
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2.2.2. Preparation of Glycerol-1,3-dichlorohydrin
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2.2.1 Polymer preparation [Chead1]
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Table 1 Reactive dyes used with their reactive systems and Colour Index numbers
Table 1 Historical tsunami that affected the western coast of India
NO
Year
Longitude °E)
Moment
Magnitude
Latitude °N)
/Location
1
326BC
2
1008
67.30
24.00
a
a
60.00
25.00
52.3b
of Loss
of Life
Earthquake
?
Earthquake
1000*
27.7b
3
1524
Gulf of Cambay
4
Rann of Kutch
6
1819
1883
Krakatau
1845
7
1945
63.00
8
2007
9
2013
5
Tsunami Source
Earthquake
7.8
Krakatau
Earthquake
>2000*
Volcanic
Rann of Kutch
7.0
Earthquake
24.50
8.1
Earthquake
101.36
-4.43
8.4
Earthquake
62.26
25.18
7.7
Earthquake
4000*
Volcanic
a
Rastogi and Jaiswal (2006) [41]
Ambraseys and Melville (1982)
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b
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Figure 1. Typical induction motor drive
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REFERENCE INFORMATION
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title to be captured as italic. For first reference before will be 12 pt and other reference before
will be 3 pt, left 0.25, hanging 0.5 and tab 0.75. Please find below the examples.
REFERENCES
All references to be cited in the text in []. For example [1]
Journal Articles:
[1]
[2]
[3]
Hebeish, A. and El-Rafie, M. H. American Dyestuff Reporter, 79(7), 1990, pp.
34.
Maganioti, A. E., Chrissanthi, H. D., Charalabos, P. C., Andreas, R. D., George,
P.N. and Christos, C. N. Cointegration of Event-Related Potential (ERP) Signals
in Experiments with Different Electromagnetic Field (EMF) Conditions. Health,
2, 2010, pp. 400-406.
Bootorabi, F., Haapasalo, J., Smith, E., Haapasalo, H. and Parkkila, S. Carbonic
Anhydrase VII—A Potential Prognostic Marker in Gliomas. Health, 3, 2011, pp.
6-12.
E-Journal Articles:
[4]
Bharti, V.K. and Srivastava, R.S. Protective Role of Buffalo Pineal Proteins on
Arsenic-Induced Oxidative Stress in Blood and Kidney of Rats. Health, 1, 2009,
pp.
167-172.
http://www.scirp.org/fileOperation/downLoad.aspx?path=Health20090100017_9
7188589.pdf&type=journal
Books:
[5]
Billmeyer, F. W. Jr. and Saltzman M. Principles of Colour Technology, 2nd
Edition. New York : John Wiley & Sons, 1981, pp. 140.
Edited Book:
[6]
Prasad, A. S. Clinical and Biochemical Spectrum of Zinc Deficiency in Human
Subjects. In: Prasad, A. S., ed., Clinical, Biochemical and Nutritional Aspects of
Trace Elements. New York : Alan R. Liss, Inc., 1982 pp. 5-15.
Conference Proceedings:
[7]
Clare, L., Pottie, G. and Agre, J. Self-Organizing Distributed Sensor Networks.
Proceedings SPIE Conference Unattended Ground Sensor Technologies and
Applications, Orlando, 3713, 1999 pp. 229-237.
Thesis:
[8]
Heinzelman, W. Application-Specific Protocol Architectures for Wireless
Networks. Ph.D. Dissertation, Cambridge: Massachusetts Institute of
Technology, 2000.
Internet:
[9]
Honeycutt,
L.
Communication
and
http://dcr.rpi.edu/commdesign/class1.html
Design
Course,
1998.
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Journal of Engineering Structures (JOES)
Volume 6, Issue 7, Jul 2015, pp. 80-92, Article ID: JOES_06_07_010
Available online at http://www.iaeme.com/JOES/issues.asp?JTypeJOES&VType=6&IType=7
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication
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TSUNAMI EMERGENCY RESPONSE
SYSTEM USING GEO-INFORMATION
TECHNOLOGY ALONG THE WESTERN
COAST OF INDIA
V. M. Patel
Civil Engineering Department, K. D. Polytechnic, Patan - 384265, Gujarat, India
M. B. Dholakia
L. D. College of Engineering, Ahmedabad - 380015, Gujarat, India
A. P. Singh
Institute of Seismological Research, Gandhinagar - 382 009, Gujarat, India
V.D. Patel
Civil Engineering Department, Government Engineering, Patan, Gujarat, India
ABSTRACT
The Makran coast is extremely vulnerable to tsunamis and earthquakes
due to the presence of three very active tectonic plates namely, the Arabian,
Eurasian and Indian plates. On 28 November 1945 at 21:56 UTC, a massive
Makran earthquake generated a destructive tsunami in the Northern Arabian
Sea and the Indian Ocean. The tsunami was responsible for loss of life and
great destruction along the coasts of Pakistan, Iran, India and Oman. In this
paper tsunami early response system created using classification of tsunami
susceptibility along the western coast of India. Based on the coastal
topographical features of selected part of the western India, we have prepared
regions susceptible to flooding in case of a mega-tsunami. Geo-information
techniques have proven their usefulness for the purposes of early warning and
emergency response. These techniques enable us to generate extensive geoinformation to make informed decisions in response to natural disasters that
lead to better protection of citizens, reduce damage to property, improve the
monitoring of these disasters, and facilitate estimates of the damages and
losses resulting from them. The classification of tsunami risk zone (susceptible
zone) is based on elevation vulnerability by Sinaga et al. (2011). We overlaid
satellite image on the tsunami risk map, and identified the region to be
particularly at risk in study area. In our study satellite images integrated with
GIS/CAD, can give information for assessment, analysis and monitoring of
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natural disaster. We expect that the tsunami risk map presented here will
supportive to tsunami early response system along the western coast of India.
Key words: Tsunami, GIS, Tsunami Risk Zone and Western Coast of India
Cite this Article: Patel, V. M., Dholakia, M. B., Singh, A. P. and Patel, V. D.
Tsunami Emergency Response System Using Geo-Information Technology
along the Western Coast of India. Journal of Engineering Structures, 6(7),
2015,
pp.
80-92.
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1. INTRODUCTION
Tsunami is a phenomenon of gravity waves produced in consequence of movement of
the ocean floor. The giant tsunami in the Indian Ocean on 26 December 2004,
claiming more than 225,000 lives (Titov et al. 2005; Geist et al. 2006; Okal &
Synolakis 2008, Singh et al. 2012) [9, 32, 47], has emphasized the urgent need for
tsunami emergency response systems for various vulnerable coastlines around the
world, especially for those neighbouring the Indian Ocean. The second deadliest
tsunami prior to 2004 in South Asia occurred on 28 November 1945 (Heck 1947;
Dominey-Howes et al. 2007; Heidarzadeh et al. 2007; Jaiswal et al. 2009; Hoffmann
et al. 2013) [8, 12, 14, 18, 22]. It originated off the southern coast of Pakistan and was
destructive in the Northern Arabian Sea and caused fatalities as far away as Mumbai
(Berninghausen 1966; Quittmeyer & Jacob 1979; Ambraseys & Melville 1982;
Heidarzadeh et al. 2008; Jaiswal et al. 2009) [1, 2, 4, 15, 23]. More than 4000 people
were killed by both the earthquake and the tsunami (Ambraseys & Melville 1982).
Several researchers have different estimates about the location of the earthquake
epicentre. Heck (1947) reported the epicentre at 25.00º N and 61.50º E. According to
Pendse (1948), [38] the epicentre was at 24.20º N and 62.60º E, about 120 km away
from Pasni. Ambraseys and Melville (1982) reported the epicenter at 25.02º N and
63.47º E. By recalculating the seismic parameters of the 1945 earthquake, Byrne et al.
(1992) suggested that the epicentre was at 25.15º N and 63.48º E, which is used in the
present study. The earthquake mainly affected the region between Karachi and the
Persian border. In Karachi, ground motions lasted approximately 30 sec, stopping the
clock in the Karachi Municipality Building and interrupting the communication cable
link between Karachi and Muscat (Oman). According to Pendse (1948), the tsunami
that was generated reached a height of 12–15 m in Pasni and Ormara on the Makran
coast and caused great damage to the entire coastal region of Pakistan. However,
several researchers have estimated the tsunami height of about 5–7 m near Pasni
(Page et al. 1979; Ambraseys & Melville 1982; Heidarzadeh et al. 2008b) [16]. The
tsunami wave was observed at 8:15 am on Salsette Island, i.e. Mumbai, and reached a
height of 2 m (Jaiswal et al. 2009; Newspaper archives, Mumbai).
1.1. Importance of Geo-Information Technology for Tsunami Risk
Visualization
The tsunami risk visualization created by Geo-Information technologies of
Geographic Information Systems (GIS), Remote Sensing (RS) and Computer Aided
Design (CAD) are powerful tools for conveying information to decision-making
process in natural disaster risk assessment and management. Visualization is the
graphical presentation of information, with the goal of improving the viewer
understands of the information contents. Comprehension of 3D visualized models is
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easier and effective than 2D models. 3D visualization models are important tools to
simulate disaster from different angle that help users to comprehend the situation
more detailed and help decision makers for appropriate rescue operations. 3D
visualizations are tools for rescue operations during disasters, e.g., cyclone, tsunami,
earthquake, flooding and fire, etc. 3D visualization has a big potential for being an
effective tool for visual risk communication at each phase of the decision-making
process in disaster management (Kolbe et al. 2005; Marincioni, 2007; Zlatanova,
2008) [24, 27, 53]. 3D visualisations have the potential to be an even more effective
communication tool (Zlatanova et al. 2002; Kolbe et al. 2005) [51]. Previous studies
have shown that the presentation of hazard, vulnerability, coping capacity and risk in
the form of digital maps has a higher impact than traditional analogue information
representations (Martin and Higgs, 1997). Graphical representation significantly
reduces the amount of cognition effort, and improves the efficiency of the decision
making process (Christie, 1994), therefore disaster managers increasingly use digital
maps. Better disaster management strategies can be designed by visualization.
Table 1 Historical tsunami that affected the western coast of India
NO
Year
Longitude °E)
Latitude °N)
/Location
1
326BC
67.30
24.00
2
1008
60.00a
25.00a
52.3b
27.7b
3
1524
Gulf of Cambay
4
Rann of Kutch
6
1819
1883
Krakatau
1845
7
1945
63.00
8
2007
9
2013
5
Moment
Magnitude
Tsunami Source
of Loss
of Life
Earthquake
?
Earthquake
1000*
Earthquake
7.8
Krakatau
Earthquake
>2000*
Volcanic
Rann of Kutch
7.0
Earthquake
24.50
8.1
Earthquake
101.36
-4.43
8.4
Earthquake
62.26
25.18
7.7
Earthquake
4000*
Volcanic
a
Rastogi and Jaiswal (2006) [41]
Ambraseys and Melville (1982)
*
Both by earthquake and tsunami: Ambraseys and Melville, 1982; Bilham, 1999; Byrne et al.,
1992; Dominey-Howes et al., 2006; Heck, 1947; Merewether, 1852; Murty and Rafiq, 1991;
Murty and Bapat, 1999; Okal et al. 2006; Paras-Carayannis, 2006; Pendse, 1946; Rastogi and
Jaiswal, 2006; Quittmeyer and Jacob, 1979; Walton, 1864; National Oceanic and
Atmospheric Administration (NOAA); United States Geological Survey (USGS); Jaiswal et
al. 2011; Jaiswal et al. 2008 [5, 6, 7, 22, 28, 29, 34, 39, 48]
b
The advances in GIS/CAD and RS supported visualization have a potential to
improve the efficiency of disaster management operations by being used as a risk
communication tool. 3D models particularly the city and building models are created
by CAD software and scanned into computer from real world objects. In this study,
classification of tsunami risk zones and tsunami risk 3D visualization created in
GIS/RS and CAD environments. We except that the results presented here will be
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supportive to the tsunami emergency response system and useful in planning the
protection measures due to tsunami.
1.2. Emergency Response System along Coast of Gujarat
Gujarat state has the longest coastline in India, and has massive capital and
infrastructure investments in its coastal regions (Singh et al., 2008) [44]. With rapid
developmental activities along the coastline of Gujarat, there is a need for preparing
tsunami risk 3D visualizations database using geo-information technology. The coast
of Gujarat is prone to many disasters in past (Singh et al., 2008). Some of the most
devastating disasters that have struck the state in the last few decades include: the
Morbi floods of 1978; the Kandla (port) cyclone of 1998; the killer earthquake in
Kutch, January 26th 2001; and the flash floods in south Gujarat in 2005 and in Surat
in 2006. Also in the past the coast of Gujarat was affected by tsunami (Jaiswal et al.,
2009; Singh et al., 2012, Patel et al., 2014) [36, 37, 45]. Visualization is the graphical
presentation of information, with the goal of improving the viewer understands of the
information contents. Comprehension of 3D visualized models is easier and effective
than 2D models. 3D visualization models are important tools to simulate disaster from
different angle that help users to comprehend the situation more detailed and help
decision makers for appropriate rescue operations. 3D visualizations are tools for
rescue operations during disasters, e.g., cyclone, tsunami, earthquake, flooding and
fire, etc (Patel et al., 2013) [35].
Figure 1 Location of tsunami forecast points along the west coast of India, Pakistan, Iran and
Oman
2. DATA USED AND TSUNAMI MODELING
In the present study tsunami forecast stations were selected for output of tsunami
simulation along the coast of India, Pakistan, Oman and Iran. Most of the tsunami
forecast stations were selected in such a way that sea depth is less than 10.0m to better
examine tsunami effect (Onat and Yalciner, 2012) [33]. The location of tsunami
forecast points along the west coast of India including Pakistan, Iran and Oman are
shown in Figure 1. Bathymetry and elevation data are the principal datasets required
for the model to capture the generation, propagation and inundation of the tsunami
wave from the source to the land. The bathymetry database taken from General
Bathymetric Chart of the Oceans (GEBCO) 30 sec is used for tsunami modeling and
the topography data taken from SRTM 90 m resolution is used for preparation of the
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inundation map. The bounding coordinates selected are 55°76° E longitudes and 10°
– 30° N latitudes. The rupture parameters are taken from Byrne et al. (1992), which
was used to model the source of the 1945 earthquake in this study (Table 2). The
initial wave amplitude (elevation and depression) for the source is computed using
Okada’s (1985) [31] method. The water elevation in the source is about 3 m, and the
depression is about 1 m.
Furthermore, tsunami simulation basically aims to calculate the tsunami heights
and its arrival times in space and time. The tsunami is assumed as a shallow water
wave, where wavelength is much larger than the depth of the sea floor. The governing
equations in tsunami numerical modeling are non-linear forms of shallow water
equations with a friction term. The formulas are solved in Cartesian coordinate system
(Imamura et. al, 2006) [19, 20, 21, 42].
Table 2 The rupture parameter of 1945 Makran earthquake provided by Byrne et al. (1992)
Epicenter of
Earthquake
Fault
length
Fault
width
Strike
angle
Rake
angle
Dip
angle
Slip
magnitude
Focal
depth
Latitude
Longitude
(km)
(km)
°
°
°
(m)
(km)
25.15° N
63.48° E
200
100
246
90
7
7
15
3. RESULTS AND DISCUSSION
Tsunami snapshots show that the 1945 Makran event affected all the neighboring
countries including Iran, Oman, Pakistan, and India (Figure 2). The results of initial
tsunami generation based on the fault parameters given by Byrne et al. (1992) are
shown in Figure 2(a). Tsunami snapshots (Figures 2(b), 2(c), 2(d), 2(e) and 2(f)) show
the estimated wave propagation at t= 30, 60, 90, 120 and 150 minutes after the
tsunamigenic earthquake, respectively. Along the southern coast of Pakistan, the
tsunami wave reaches Pasni in about 5 to 15 minutes, Ormara in about 60 minutes,
and Karachi in about 110 minutes. While along the southern coast of Iran, the tsunami
wave reaches Chabahar in about 30 to 35 minutes and Jask in about 70 to 75 minutes.
After the earthquake, the tsunami wave reaches the coast of Oman namely at Muscat
in about 40 minutes, Sur in about 30 to 40 minutes, Masirah in about 60 to 70
minutes, Sohar in about 80 minutes, and Duqm in about 130 minutes. Furthermore,
the tsunami wave reaches the western coast of India along the Gulf of Kachchh in
about 240 minutes, Okha in about 185 minutes, Dwarka in about 150 minutes,
Porbandar in about 155 minutes, Mumbai in about 300 minutes, and Goa in about 215
minutes. It is also observed that the distance from epicentre to Mumbai is less than
Goa, but the arrival time of the first tsunami wave at the Mumbai is more than Goa. It
could be due to the fact that Mumbai offshore is shallower that Goa and also due to
the directivity of tsunami wave propagation. It is well known that most of the
tsunami’s energy travels perpendicular to the strike of the fault which is due to
directivity (Ben-Menahem and Rosenman 1972; Singh et al., 2012, Patel et al., 2014)
[3]. Due to this effect, most of the tsunami energy propagates in the direction. The
tsunami travel time map is shown in Figure 3.
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Figure 2 Results of the tsunami generation and propagation modeling
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Figure 3 Tsunami travel time contour map
Figure 4 shows the maximum calculated tsunami run-ups along western coast of
India for a tsunami simulation of 360 minutes. The simulated results show that the
maximum tsunami height is about 5–6 m near the southern coast of Pakistan, which is
corroborated with the previous researchers in the same region (Page et al., 1979;
Ambraseys and Melville, 1982; Heidarzadeh et al., 2008) [17]. The maximum
calculated tsunami run-ups were about 0.7–1.1 m along coast of Oman, 0.7–1.35 m
along the western coast of India, 0.5–2.3 m along the southern coast of Iran and 1.2–
5.8m along the southern coast of Pakistan, respectively. The tsunami run-up along the
southern coast of Pakistan is far larger than that along the other coasts and may be due
to directivity of the tsunami.
It is believed that the digital topographical data is very important in detecting
tsunami prone area. The SRTM data are used to provide digital elevation information.
Based on the processed SRTM data in GIS/CAD, all low-lying coastal areas
potentially at risk of tsunami flooding have been identified. The classification of
tsunami risk zone is based on elevation vulnerability followed by Sinaga et al. (2011)
[43]. However, for high resolution mapping of tsunami risk zone along the coastal
region, very high resolution topographical data and satellite images are needed. In this
study, we developed the methodology for creation of 3D infrastructure located in
tsunami risk zones using easily available and low cost Google earth images and
SRTM data in AutoCAD Map 3D software [40]. The coastal area of Okha Okha
potentially affected at different tsunami flooding scenarios shown in Figure 5. The 3D
tsunami risk model of Okha at different viewing angles is presented in Figures 6 (a)(c). A red, blue or green colour scheme was used to indicate the respective
susceptibility to tsunami risk as shown in Figure 6 It shows structures that are
classified as very high risk, high risk and medium risk based on tsunami run-up
height.
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Figure 4 Maximum calculated tsunami run-ups along western coast of India
Figure 5 Coastal area of Okha potentially affected at different sea level rise scenarios
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Figure 6 Visualization of 3D tsunami risk model of Okha with different viewing angles
4. CONCLUSION
Early warning technologies have greatly benefited from recent advances in geoinformation technologies and an improved knowledge on natural hazards and the
underlying science. Natural disaster management is a complex and critical activity
that can more effectively with the support of geo-information technologies and spatial
decision support systems. The 1945 Makran tsunamigenic [13, 30, 46] earthquake is
modeled using rupture parameters suggested by Byrne et al. (1992). In most cases, the
coastal regions which are far from the source have smaller tsunami height and longer
tsunami travel times compared with the coastal regions near the source that have
higher tsunami heights and shorter tsunami travel times. As a part of a tsunami
emergency response system the 3D coastal maps should be produced for countries in
the vicinity of the MSZ, namely, Pakistan, India, Iran and Oman. The lessons learnt
from the Dec 2004 tsunami could be used for future planning. Ports, jetties, estuarine
areas, river deltas and population in and around the coast of Pakistan, India, Iran and
Oman could be protected with proper methods of mitigation and disaster
management. In the future scientists/researchers need to focus on 3D visualization and
animation of tsunami risk. The study was performed to show the advantages of 3D
GIS/CAD models and satellite images in tsunami risk assessment of the Okha coast,
Gujarat. The main aim of the 3D Okha model is to visualize each building’s tsunami
risk level which improves decision maker’s understanding of the disaster level.
Merging of SRTM elevation data with satellite images is suitable for tsunami risk
zone classification. Combining the advanced computer aided modeling, GIS based
modeling, marine parameter measurements by ocean bottom seismometers and
satellite, installations of tide gauges and tsunami detection systems and also using
conventional and traditional knowledge, it is possible to develop a suitable tsunami
disaster management plan.
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5. ACKNOWLEDGEMENTS
The authors thank Profs Andrey Zaytsev, Ahmet Yalciner, Anton Chernov, Efim
Pelinovsky and Andrey Kurkin for providing NAMI-DANCE software and for their
valuable assistance in tsunami numerical modelling of this study. Profs. Nobuo Shuto,
Costas Synolakis, Emile Okal, Fumihiko Imamura are acknowledged for invaluable
endless collaboration. The VMP is grateful to Dr. B. K. Rastogi, Director General,
Institute of Seismological Research (ISR) for permission to use of ISR library and
other resource materials. APS is thankful to Director General, ISR, for permission and
encouragement to conduct such studies for the benefit of science and society.
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