Identification of Seismic Vulnerability Zones based on Land Use Condition

Abstract

Due to urbanization, the vulnerability is increased in cities and the scale of disaster from earthquake is increased in major cities. Therefore, developing seismic vulnerability map for urbanized cities is very important. Mandalay city is not only one of the most earthquake-prone regions but also the most urbanized and dense population in the Republic of the Union of Myanmar. This study examines the seismic vulnerability assessment of Mandalay city based on the land use conditions by utilizing analytic hierarchy process (AHP) and Geographic Information System (GIS). The land use data was collected by doing field survey and classified into 20 types of study area regarding to the Myanmar National Building Code (MNBC) and field condition. The importance of each criterion (land use types) are determined by using subjective opinion made by authorized persons from Mandalay City Development Committee (MCDC) because the seismic vulnerability levels may be different based on land use conditions. The consistency ratios (CR) are also checked for reliability of weighted criteria. The final seismic vulnerability map is developed by overlapping the weighted land use map with building density and population density map by using aggregation method in GIS. It will be very useful for making a national emergency plan for earthquakes to mitigate the seismic risk due to the future earthquakes.

Full text

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American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
ISSN (Print) 2313-4410, ISSN (Online) 2313-4402
© Global Society of Scientific Research and Researchers
http://asrjetsjournal.org/
Identification of Seismic Vulnerability Zones based on
Land Use Condition
Zin Zin Nwea*, Kay Thwe Tunb
a Department of Civil Engineering, Mandalay Technological University, The Republic of the Union of Myanmar
b Department of Civil Engineering, Mandalay Technological University, The Republic of the Union of Myanmar
aEmail: zinzinnwe.civil@gmail.com
bEmail: kaythwetun.pm@gmail.com
Abstract
Due to urbanization, the vulnerability is increased in cities and the scale of disaster from earthquake is increased
in major cities. Therefore, developing seismic vulnerability map for urbanized cities is very important.
Mandalay city is not only one of the most earthquake-prone regions but also the most urbanized and dense
population in the Republic of the Union of Myanmar. This study examines the seismic vulnerability assessment
of Mandalay city based on the land use conditions by utilizing analytic hierarchy process (AHP) and Geographic
Information System (GIS). The land use data was collected by doing field survey and classified into 20 types of
study area regarding to the Myanmar National Building Code (MNBC) and field condition. The importance of
each criterion (land use types) are determined by using subjective opinion made by authorized persons from
Mandalay City Development Committee (MCDC) because the seismic vulnerability levels may be different
based on land use conditions. The consistency ratios (CR) are also checked for reliability of weighted criteria.
The final seismic vulnerability map is developed by overlapping the weighted land use map with building
density and population density map by using aggregation method in GIS. It will be very useful for making a
national emergency plan for earthquakes to mitigate the seismic risk due to the future earthquakes.
Keywords: analytic hierarchy process; GIS; land use; vulnerability.
1. Introduction
Myanmar is located at a very active tectonic area, which includes the subduction zone and the active Sagaing
fault. Sagaing fault extending more than 1,000 km across entire Myanmar in N-S direction forms the
transcurrent N-E boundary of the Indian Plate accommodating its northerly motion between the Burma and
Sunda microplates. It is a typical continental dextral strike-slip fault with a slip-rate of 18 mm/year and is
comparable to other well-known faults such as the San Andreas Fault in California, U.S., North Anatolian Fault
in Turkey and the Great Sumatra Fault in Indonesia. Historically and within the instrumental period, the Sagaing
fault has produced a number of large earthquakes some of which has caused significant damage [1]. Historical
earthquakes occurring along Sagaing fault with notable magnitudes are shown in Figure 1.

American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102
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------------------------------------------------------------------------
* Corresponding author.
E-mail address: zinzinnwe.civil@gmail.com.
Figure 1: map of historical earthquakes after the year 1906
Mandalay lies very closed to the dextral Sagaing fault (about 7 km in the west), a tectonic plate boundary
between the India and Sunda plates. In the historical records, many earthquakes happened in and around
Mandalay area. The most distinct events near Mandalay area are Innwa earthquake (March 23, 1839) and
Sagaing earthquake (July 16, 1956). Due to Innwa earthquake (maximum intensity of MMI IX), about three to
four hundred casualties were resulted in Mandalay area and many buildings including pagodas were severely
damaged. The Sagaing earthquake with (Mw=7.0) magnitude also caused some considerable damage and
casualties [9]. Therefore, developing seismic vulnerability assessment for Mandalay city is very crucial. This
study examines the seismic vulnerability assessment based on the actual land use conditions by using AHP-GIS
to minimize the losses due to the future earthquakes.
2. Description on Study Area
Mandalay, the second largest city and third capital of the Republic of the Union of Myanmar, is located in the
central dry zone of Myanmar by the Ayeyarwaddy River at 21.98° North, 96.08° East, 80 meters (260 feet)
above sea level. Mandalay features noticeably warmer and cooler periods of the year. The highest reliably
recorded temperature in Mandalay is 45.6 °C (114.1 °F) and the lowest is 5.6 °C (42.1 °F). [5] Its population has
about 1.3 million for five townships and several fields, e.g. urban development and industrialisation, rapidly
increased. As of 2012, Mandalay City Development Committee (MCDC) divided Mandalay City into 7

American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102
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townships which are Amarapura, Aung Myay Tha Zan, Chan Aye Tha Zan, Chan Mya Tha Zi, Maha Aung
Myay, Pyi Gyi Ta Gon, and Patheingyi townships. Only the 5 central townships as shown in Figure 2 are
included in this study because the left townships, Amarapura and Patheingyi, were added recently into the city
area. Table 1 shows some information of Mandalay city such as population, number of buildings and area of
each township, etc.
Figure 2: map of study area
Table 1: Mandalay city information
Townships
Area
(km2)
No. of
Quarters
Households
No. of
Buildings
Population
Male
Female
Both Sex
Aung Myay Thar Zan
25.81
18
38 907
49 233
130 162
136 203
266 365
Chan Aye Thar Zan
11.70
20
28 785
24 452
93 216
104 096
197 312
Maha Aung Myay
14.45
18
37 385
43 231
116 802
123 954
240 756
Chan Mya Tharzi
26.13
14
43 520
66 318
136 811
146 494
283 305
Pyi Gyi Ta Gon
33.18
16
36 492
49 948
120 756
116 639
237 395
Total
112.27
86
185 089
233 182
597 747
627 386
1 225 133
Study Area
Myanmar
Mandalay City
Mandalay Region

American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102
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3. Methodology
In this study, Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) are used to make
the earthquake vulnerability assessment. To determine the importance of criteria and sub-criteria, analytic
hierarchy process (AHP) model, one of multi criteria decision making method that was originally developed by
Prof. Thomas L. Saaty, is used. AHP is a method to derive ratio scales from paired comparisons. The input can
be obtained from actual measurement or from subjective opinion [6].
The weight of each factor is determined regarding its level of importance as shown in Table 2 and introduced by
Saaty (1977). Some small inconsistency in judgment is allowed because human is not always consistent. If the
value of Consistency Ratio (CR) is smaller or equal to 10%, the inconsistency is acceptable. If the Consistency
Ratio is greater than 10%, the subjective judgments need to revise. The Consistency Ratio is a comparison
between Consistency Index (CI) and Random Consistency Index (RI). The Consistency Index (CI) is defined by
Saaty (2000) as follows:
CI = (

_max

N)/(N

1) (1)
where max is the largest or principal eigenvalue of the pairwise comparison matrix and N is the order of the
matrix. Saaty (1980) has identified the average random consistency index (RI) as shown in Table 3.
Table 2: Scale of preference between two parameters in AHP (Saaty, 1977)
Intensity of
importance
Degree of
preference
Explanation
1
Equally
Two factors contribute equally to the objective
3
Moderately
Experience and judgment slightly to moderately favor one factor over another
5
Strongly
Experience and judgment strongly or essentially favor one factor over another
7
Very strongly
A factor is strongly favored over another and its dominance is showed in
practice
9
Extremely
The evidence of favoring one factor over another is of the highest degree
possible
2, 4, 6, 8
Intermediate
Used to represent compromises between the preferences in weights 1, 3, 5, 7
and 9
Reciprocals
Opposites
Used for inverse comparison
Table 3: Random inconsistency indices (RI) for n=1, 2, 3… 12 (Saaty, 1980, 2000)
N
1
2
3
4
5
6
7
8
9
10
11
12
RI
0.00
0.00
0.58
0.90
1.12
1.24
1.32
1.41
1.45
1.49
1.52
1.54

American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102
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4. Identification of Seismic Vulnerability Zones based on Land Use Condition
4.1. Generation of Land Use Map
The 2014 satellite image was used to digitize the land use data and Myanmar National Building Code (MNBC)
was used to classify the land use condition. Firstly, polygons are drawn based on visual interpretation of land
use. Secondly, field survey was done to collect the actual information for detail land use types. This survey was
done with the help of remote sensing department from Mandalay Technological University for six months.
Finally, land use was classified as 20 types (Table 4) such as residential, commercial, education, and hotel, etc.
After making the field survey, land use types were assigned to attribute table and combined the polygons in the
same land use types. Figure 3 and 4 show the main and detail land use conditions of Mandalay city based on
field survey and MNBC.
Table 4: Land use classifications based on field survey and MNBC
Items
Main Group
Land Use Details
Remarks
I
Residential
Only Resident
Public houses, government service’s houses
Mixed
Resident + Store, Entertainment, Cinema
II
Commercial
Market
Shopping mall, Private Bank, Restaurant, Wedding hall, Car
show room
Private Hospital
Private hospitals and clinics
Private School
Private Pre-school, Primary and High school, Training center
Hotel
Hotel
III
Governmental
Education
Basic Education Primary, Middle and High School, Institute,
University, Cripple, Training
Office
Police station, Bank, Audit, Township Admin,
Government
Hospital
Public hospital, Sangha hospital, Workers’ hospital, Central
women hospital, Children hospital etc.
Military
Military
IV
Industrial
Home industry
Oil, car workshop, trucker industry, peanut mill, ware house,
purified water plant, Timber plant, Juice, detergent, soap
Hazardous industry
Paper industry, sugar, iron, candle, leather, gas, Plastic,
alcohol, concrete, textile, fertilizer
V
Religious
Monastery
Monastery
Pagoda
Pagoda
Community hall
Church, Chinese temple, Dhamma hall, etc.
VI
Public and
Social
Station
Express station, Railway station
Stadium
Sport stadium
Museum
Museum
Recreational zones
Playground, park, Golf, Skate
VII
Open spaces
Waterbody, field,
etc.
Waterbody, field, etc.
4.2. Making the Criteria to develop vulnerable zones
To find the different vulnerability level based on land use changes, analytic hierarchy process (AHP) model
under multi criteria decision making (MCDM) is used. Waterbody and open space are not considered in
assessing vulnerability. Firstly, pairwise comparison matrixes are developed for criteria weights and then sub-
criteria weights are calculated based on the expert judgements by the authorized persons from Mandalay City
Development Committee (MCDC). Finally, the total weights are estimated by multiplying the criteria weights
and sub-criteria weights respectively. Pairwise comparison matrix, weighted values, and consistency ratio for

American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102
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criteria and sub-criteria are shown in Table 5 and Table 6 respectively. All CR values for all criteria and sub-
criteria are less than 0.1 hence it can be said weight assigning is reasonable. Table 7 shows the total weights for
assigning the attribute table.
Figure 3: detail land use map
Figure 4: main land use map
Table 5: Pairwise comparison matrix, criteria weights
Criteria
C1
C2
C3
C4
C5
C6
Weighted values
Residential
1
2
3
4
5
6
0.379
Commercial
1/2
1
2
3
4
5
0.249
Governmental
1/3
1/2
1
2
3
4
0.160
Industrial zones
1/4
1/3
1/2
1
2
3
0.102
Religious
1/5
1/4
1/3
1/2
1
2
0.065
Public and Social
1/6
1/5
1/4
1/3
1/2
1
0.043
Consistency Ratio (CR): 0.027 < 0.1  Acceptable
Table 6: Pairwise comparison matrix, sub-indicator weights
Sub-indicator
1
2
3
4
Weighted values
Residential
Only Residents
1
1/3
0.250
Mixed
3
1
0.750
Consistency Ratio : 0.0 < 0.1  Acceptable

American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102
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Commercial
Market
1
2
3
4
0.466
Private Hospital
1/2
1
2
3
0.277
Private School
1/3
1/2
1
2
0.161
Hotel
1/4
1/3
1/2
1
0.096
Consistency Ratio : 0.015 < 0.1  Acceptable
Governmental
Education
1
2
3
4
0.466
Office
1/2
1
2
3
0.277
Government Hospital
1/3
1/2
1
2
0.161
Military
1/4
1/3
1/2
1
0.096
Consistency Ratio : 0.015 < 0.1  Acceptable
Industrial zones
Home industry
1
1/4
0.200
Hazardous industry
4
1
0.800
Consistency Ratio : 0 < 0.1  Acceptable
Religious
Monastery
1
2
3
0.539
Pagoda
1/2
1
2
0.297
Community hall
1/3
1/2
1
0.164
Consistency Ratio : 0.01 < 0.1  Acceptable
Public and Social
Station
1
3
5
3
0.512
Stadium
1/3
1
3
2
0.238
Museum
1/5
1/3
1
1/3
0.078
Recreational zones
1/3
1/2
3
1
0.172
Consistency Ratio : 0.049 < 0.1  Acceptable
Table 7: Assigning total weights by using AHP model
No.
Criteria
Criteria
Weights
Sub-criteria
Sub-indicator Weights
Total Weights
1
Residential
0.379
Residential
0.250
0.095
Mixed
0.750
0.284
2
Commercial
0.249
Market
0.466
0.116
Private Hospital
0.277
0.069
Private School
0.161
0.040
Hotel
0.096
0.024
3
Government
0.160
Education
0.466
0.075
Office
0.277
0.044
Government Hospital
0.161
0.026
Military
0.096
0.015
4
Industrial
zones
0.102
Home industry
0.200
0.021
Hazardous industry
0.800
0.082
5
Religious
0.065
Monastery
0.539
0.036
Pagoda
0.297
0.020
Community hall
0.164
0.011
6
Public and
Social
0.043
Station
0.512
0.022
Stadium
0.238
0.010
Museum
0.078
0.003
Recreational zones
0.172
0.007
7
Open Spaces
0

American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102
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4.3. Identification of Seismic Vulnerability Zones
The final seismic vulnerability map based on the actual land use condition is shown in Figure 7. To develop the
final seismic vulnerability map, the weighted land use map is integrated with population density map (Figure 5)
and building density map (Figure 6). The importance of criteria is evaluated based on the analytic hierarchy
process (AHP) method developed by Thomas L Saaty. The weighted values of each thematic layer are shown in
Table 8. The features of each thematic map are also normalized between 0 and 1 to ensure that no layer exerts
an influence beyond its determined weight. Normalization is carried out for the features using the relation:
R_nrm = (R_i

R_min)/(R_max

R_min) (2)
where Rnrm, Rmin and Rmax denotes the, normalized, assigned minimum and maximum ranks respectively.
Figure 5: population density (PD) map
Figure 6: building density (BD) map
Table 9 shows the normalized ranks of each thematic layer for seismic vulnerability assessment. The weighted
values of land use condition are considered in calculating the normalized ranks to classify the vulnerable zones
of the study area. After defining the weighted values and the normalized ranks of all criteria, all criteria layers
are integrated with one another through GIS using weighted aggregation method to identify the seismic
vulnerability map (SVM) as
SVM = [LU_w.LU_r+PD_w.PD_r+BD_w.BD_r]/

w (3)
where w represents the normalized weight of a theme and r is the normalized rank of a feature in the theme.

American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102
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Table 8: Weighted values of each thematic layer
Land Use
Building Density
Population Density
Criteria Weight
Land Use (LU)
1
2
3
0.539
Building Density (BD)
1/2
1
2
0.297
Population Density (PD)
1/3
1/2
1
0.164
Consistency Ratio : 0.01 < 0.1  Acceptable
Table 9: Normalized ranks for seismic vulnerability assessment
Themes
Attributes
Rank
Normalized
ranks
Land Use Types
Weighted values
Land Use Condition
Waterbody, field, etc.
0
1
0.000
Museum
0.003
2
0.053
Recreational
0.007
3
0.105
Stadium
0.010
4
0.158
Community hall
0.011
5
0.211
Military
0.015
6
0.263
Pagoda
0.020
7
0.316
Home industry
0.021
8
0.368
Station
0.022
9
0.421
Hotel
0.024
10
0.474
Government Hospital
0.026
11
0.526
Monastery
0.036
12
0.579
Private School
0.040
13
0.632
Office
0.044
14
0.684
Private Hospital
0.069
15
0.737
Education
0.075
16
0.789
Hazardous industry
0.082
17
0.842
Only Resident
0.095
18
0.895
Market
0.116
19
0.947
Mixed (Resident + Store)
0.284
20
1.000
Population Density
0 - 500
1
0
500 - 5000
2
0.20
5000 - 10000
3
0.40
10000 - 15000
4
0.60
15000 - 25000
5
0.80
25000 - 55084
6
1.00
Building Density
1 - 1000
1
0
1001 - 2000
2
0.25
2001 - 3000
3
0.50
3001 - 4000
4
0.75
4001 - 7075
5
1.00

American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102
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Figure 7: seismic vulnerability zones map of Mandalay city
5. Discussion and Conclusion
The seismic vulnerability is different depending on the land use changes. To estimate the different vulnerability
levels based on land use changes, analytic hierarchy process (AHP) model under multi criteria decision making
(MCDM) was used by combining with Geographic Information System (GIS). Land use map was developed by
doing the actual field survey depending on the 2014 satellite image. Land use conditions were classified
regarding to the Myanmar National Building Code (MNBC) and field condition of the study area. The
importance of criteria weights was defined by the authorized persons from Mandalay City Development
Committee (MCDC). The weighted values of land use condition were used in calculating the normalized ranks
to classify the vulnerable zones of the study area. The population density map was developed from the 2014
census data based on each quarter. The number of buildings was counted depending on the 2014 satellite image
and the building density was estimated by dividing the total number of buildings into each area.
The seismic vulnerability map was developed by integrating the weighted land use map, building density map
and population density map in GIS. Combination of AHP model and GIS tools is very convenient in developing
vulnerable zones due to earthquake. This seismic vulnerability map is very useful for estimating the seismic
risk and also making disaster mitigation plans to reduce the seismic risk for Mandalay city. As land use
condition was in 2014-2015, the future vulnerability should be calculated by using future land use condition and
future population data to update the information. Depending on these results, the detail investigation should be
done in the most vulnerable areas to mitigate the seismic risk due to the future earthquakes.

American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102
11
Acknowledgements
The authors wish like to appreciate to authorized persons from Mandalay City Development Committee
(MCDC). Special thanks go to Dr. Kyaw Zaya Htun, Lecturer, Department of Remote Sensing, Mandalay
Technological University for his valuable guidance and support. The authors would also like to thank all of the
teachers from Department of Civil Engineering and Remote Sensing Department, Mandalay Technological
University. Finally, the authors would like to thank everyone who assisted this investigation.
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