90 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,bDepartment 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. ------------------------------------------------------------------------ * Corresponding author. http://asrjetsjournal.org/ American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102 91 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. 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. https://en.wikipedia.org/wiki/Irrawaddy_River American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102 92 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 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 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 St d M M d l M d l American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102 93 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 = (λ_max − N)/(N − 1) (1) where λmax 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 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. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102 94 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 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102 95 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 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 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102 96 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 : 0.093 < 0.1 ⇒ Acceptable 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 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 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102 97 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. 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 98 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]/Σw (3) where w represents the normalized weight of a theme and r is the normalized rank of a feature in the theme. 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 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) (2016) Volume 23, No 1, pp 90-102 99 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 90-102 100 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 90-102 101 Acknowledgements The authors wish like to appreciate to the 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. References [1] Atakan K., Tun S.T., and Thant M. ‘Seismotectonics of the Sagaing Fault in Myanmar’, Istanbul Technical University Turkey, www.panaf.itu.edu.tr, 8-10 October 2012. [2] F. Rezaie and M. 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