Earthquake Risk Assesment of Urban Quetta, using Multi-criteria Anlaysis

Earthquake Risk Assesment of Urban Quetta, using Multi-criteria Anlaysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Earthquake Risk Assesment of Urban Quetta, using Multi-criteria Anlaysis Ainuddin Syed, Chamawong Suriyachan, Ariya Aruninta, Routray J.K, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4560765/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The capital of Balochistan province Quetta is surrounded by mountain ranges with many active seismic faults. The province is subjected to many earthquakes in the past including the deadliest earthquake of 1935. The objective of this paper is to carry out earthquake risk assessment of Quetta city using multi-criteria analysis. For the purpose, the primary data was collected from 400 households using stratified random sampling technique with proportionate allocation and secondary data from USGS and Pakistan Bureau of Statistics. SPSS, GIS and Arc-GIS were used to generate the vulnerability, hazard and risk maps applying analytical hierarchy process (AHP) and weighted linear combination (WLC) methods. The results reveal that Quetta is highly vulnerable to earthquake risk in the future; its geology coupled with the human dimension indicates indicate that impacts would be more disastrous in future events. Results of the final earthquake risk map shows that five out of thirteen Zones (Hazargunji, Quetta East, Kharot Abad, Samungli, and Quetta North) are at high risk. Four Zones (Centrum of Quetta, Pashtoon Abad, Saryab, and Hazara Town) are at medium risk. Only three Zones (Quetta Cantt, Satellite Town, and Jinnah Town) are at low risk. The proposed risk map of Quetta city may be used for risk communication, decision making, land use planning and development of critical infrastructure. The paper further recommends the map to be utilized as guide for emergency response systems, and develop mitigation plans including enforcement of building codes, improve emergency response activates and educate people on earthquake preparedness. Vulnerability Hazard Risk Assessment Earthquakes Balochistan Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction From 2000–2019, natural disasters have killed 1.23 million people, 4.2 billion got affected along with economic damage of 2.97 trillion US $ around the globe (Page, T. et al., 2022). Economic, environmental and social costs associated with such disasters are being observed and recorded in the recant major earthquakes occurred in Indonesia in 2004, Pakistan 2005, China 2008, Haiti 2010, Japan 2011, Nepal 2015, Ecuador 2016 and Turkey 2023. These events have exerted pressure on nations to develop appropriate cost-effective earthquake risk assessment tools particularly in seismic prone developing countries (Khan et al., 2018 , & Mehdi Boukri et al., 2018). CRED database for disasters indicates that the reported number of natural disasters have been increased enormously from the beginning of 19th century till date. (Sarkar, 2017 ). A natural hazard only becomes a disaster when it affects the exposed and vulnerable population and infrastructures (Adger, 2006 ). Natural hazards like earthquakes, droughts and hurricanes are unpreventable, but their damaging impacts can be mitigated and minimized through proper planning and implementation (Rehman et al., 2014a ). Earthquakes are the most lethal type of natural disasters amongst all others especially in urban area due to its unpredictable nature (Ainuddin and Routray 2012; Bilham 2019 ; Aldrich, 2019 ). At the start of the 20th Century, 2% two percent people lived in just 14 mega-cities of the world. Currently this proportion is increased to 20% twenty percent and it will probably increase to 40% forty percent by the year 2030 (Ceferino et al., 2020 ). This is evident that cities will face problems being turned into mega-polis from metro-polis due to urbanization which is inevitable process. (Alam & Haque, 2021 ). With the increasing pace of urbanization process, cities in developing countries are more likely to be exposed to physical, economic and human losses due to the increased risk to natural hazards particularly earthquakes (Kuddus et al., 2020 ). Seismic Hazards and Pakistan Seismic hazards and their associated risks are on rise globally and becoming more disastrous in countries with poor policy measures, ignorance and corrupt practices in construction industry (Bilham, 2009 ; Armas et al., 2017). According to the EM-DAT, an average of 27,000 people are killed per year due to earthquakes. Every day, almost 4000 earthquakes of different scales occur around the planet (Guha-Sapir & Vos, 2011 ). The continent of Asia has particularly remained a permanent victim of earthquakes of different scales (Bilham, 2019 , Aldrich, 2010 ). The historical data shows that earthquake occurrence ratio between Asia and the rest of the world is 55:45 (UNISDR, 2013). Damages due to earthquakes in Asia may be linked to poverty, increasing trends in urbanization, poor construction practices, and lack of policy implementation (Aitsi-Selmi et al., 2015 ; Bilham 2009 ). Therefore, Dolce et al., ( 2006 ) argues that risk assessment of seismic hazards can support policymakers in designing risk mitigation measures that can include enforcement of building codes, emergency evacuation plans, retrofitting activities, and development of insurance pools. Earthquake hazard and vulnerability assessment aim to provide a reliable prediction of expected physical damage as well as social and economic losses due to potential earthquake events (Alizadeh et al., 2021 ). This can enhance the potential of urban planning in the context of physical infrastructures and hazard mitigation efforts (Tobin, 1999 ; Boukri et al., 2018). Pakistan is no exception to it, which is geographically prone to earthquake hazards and subjected to major earthquakes in the past with various ranks of vulnerabilities. (Halverson 2010; Bilham, 2019 ). Five major earthquakes with the range of 6.5 to 8.0 magnitudes occurred with potential human and economic losses from 1900 to 2008 (Rehman et al., 2014b ). The 2019, Mirpore earthquake raised many questions for policymakers and local administration, where, almost all the infrastructures including housing, roads, overflies and water reservoirs were completely collapsed. More than 40 people were killed, 400 people were injured and thousands of families were displaced (Barkat et al., 2022 ). Seismically, Pakistan is among the most active regions for earthquakes on the globe due to the existing collision boundaries of Eurasian, Arabian and Indian plates. The collision of the plate boundary in the north is of convergent nature as Indian plate is sub-ducting beneath the Eurasian plate, with 36–42 mm/year (Chen et al. 2000 ). In the western part of the country, the nature of the plate boundary is transform marked by the well-known active Chaman fault as shown in Fig. 1 . Arabian plate is converging at the rate of 28–33 mm/year with the Eurasian plate in the south along with subduction zone of Makran (Apel et al. 2006 ). In the recent past, major catastrophic earthquake events occurred due to the collision of these plate boundaries, which include Kangra (1905, M ~ 8.0); Quetta (1935, M ~ 7.6); Makran Tsunami (1945, M ~ 8.3); Hunza (1974, M ~ 6.2); Chamman (1992, M ~ 6.2); Kashmir (2005, M ~ 7.6); Ziarat (2008, M ~ 6.4); Hindukush (2015, Mm ~ 7.5); and Hurnai (2021, M ~ 5.9) earthquakes. Earthquake risk assessment is one of the best efforts to address the adverse impacts of seismic hazard in a particular area, especially in urban areas where population density is high. In the city of Quetta, three earlier studies related to earthquake risk assessment were conducted. The first study was conducted by (Hazard, 2012 ). In which the city was divided into thirteen zones based on socio-economic conditions, population, building typology and soil type. Three earthquake scenarios with magnitudes 6.5, 6.9, and 7.3 with epicenters that were 105, 21 and 66 km away from the center of the city of Quetta, respectively were used in the damage and loss assessment. While the second study was conducted by (Shah, 2012 ), in which probabilistic seismic hazard analysis technique was used based on area sources and augmented by line source used for the earthquake loss and damage assessment. The third study was conducted by (Rehman, Lindholm, Ahmed, & Rafi, 2014a ), which was focused only on hazard assessment using deterministic seismic risk assessment (DSHA). In these three studies, many important aspects of risk assessment and indicators like evacuation roots, open spaces, fire services, healthcare services and emergency routes were not assessed, which could make the estimations process illogical in the context of the study area. In the 2nd and 3rd studies, only earthquake hazard was focused, however, hazard assessment alone would not serve the best without taking vulnerability and exposure into account to understand the overall seismicity of the city. Therefore, to overcome these issues pointed out in the above-mentioned studies, the current study has focused on a detailed seismic risk assessment approach taking care of the missing indications and components. Because a holistic risk assessment is essential to measure hazard, vulnerability and exposer to avoid the impacts of future earthquake events and guide the policy makers (Cutter et al 2010; Khan 2007). The current study can also help disaster professionals and policy makers to focus on community level risk reduction strategies, building codes enforcement, retrofitting activities and others essential measures in line with Sendai framework and sustainable development goals (SDG’S) agendas (2015–2030). Materials and Methods 2.1 Selection of Study Area Quetta, the capital of the province is located in a very active seismic zone and frequently faced different scales of catastrophic earthquake events with various potential damages in the history. In the earthquake zonation map of Pakistan, Quetta is situated in the first zone, which is very high active zone in the context of earthquake (Ainuddin & Routray, 2012). The entire city was demolished in the (1935) Quetta earthquake with 7.6 magnitudes on reactor scale. Currently Quetta city has an area of 176 km 2 . Apart from the occurrence of an earthquake, some of the other hazards likes subsidence, surface fault, landslide, liquefaction and secondary hazard like fire following the earthquake are possible to occur. Therefore, it is important to see each vulnerable aspect of earthquake hazard. The study area is divvied into thirteen Zones based on their socioeconomic and demographic profile as shown in given in Fig. 2 . 2.2. Data Collection Methods Data was collected from both primary and secondary sources. Primary data was collected through questionnaire survey with 400 sample respondents to acquire information using proportionate allocation method. While secondary data was downloaded from the United States Geological Survey (USGS) to get the Peak Ground Acceleration (PGA) values of the study area. Geological Survey of Pakistan (GSP) provided the geological map and soil data of the study area. Population data was taken from Pakistan Bureau of Statistics (PBS) for population density map. Similarly, other necessary information was taken from literature and previous studies. Furthermore. point feature is used to collect the data on systemic vulnerability. 2.2.1 Sample Size Selection Quetta city was divided into thirteen zones based on their socioeconomic and demographic information. Based on the Arkin and Colton formula (1963) of sample size, a total of 400 sample respondents were investigated for the primary household survey. Stratified random sampling technique is employed using proportionate allocation method due to the heterogeneity of the area in terms of population in the thirteen zones. The sample size formula calculation is given below. $$\text{n}=\frac{\text{N}{\text{Z}}^{2}\times \text{P}\times (1 -\text{P})}{\text{N}{\text{e}}^{2}+{\{\text{Z}}^{2}\times \text{P}\times (1 -\text{P})\}}$$ $$\text{n}=400$$ Proportionate Allocation formula for each zone $${\text{n}}_{\text{k}}=\frac{{\text{N}}_{\text{k}}}{\text{N}} \times \text{n}$$ Where, N = Total number of Households Z = Confidence Interval (95%) = 1.96 P = Degree of Variation = 50% = 0.50 E = Margin of error = 5% = 0.05 Data was analyzed through Statistical Package for Social Sciences (SPSS), Excel, and Geographic Information System (GIS). Data related to the socio-economic and structural components are performed in SPSS software and then transferred into the GIS environment to generate required maps. Whereas indicators related to hazard assessment like Peak Ground acceleration (PGA), Fault Lines (FL) and Soil Type (ST) are performed in Arc-GIS to produce the expected maps and results. Analytical Hierarchal Process (AHP) and Weighted Linear Combination (WLC) methods are used to identify earthquake hazard, vulnerability and risk maps based on developed indicators. 2.2.2. Selection of Indicators/Parameters The parameters/indicators for this research are taken from the available literature. 24 seismic risk indicators have been selected for the risk assessment. Notably, these indicators are adopted from the studies of (Cutter, Boruff, & Shirley, 2003 ; Ainuddin & Routray, 2012 and Alam & Haque, 2021 ). These indicators with supportive literature are shown in Table, 1,2,3 and 4 respectively. Table 1 Earthquake Hazard indicators Indicators Vulnerability Level Supportive literature High Medium Low Peak Ground Acceleration > 0.410 0.351–0.410 0.311–0.350 (Alam & Haque, 2021 ; (Jena & Pradhan, 2020 and (Rezaie & Panahi, 2015 ). Faults Line 1500 m (Jena & Pradhan, 2020 ; Alam & Haque, 2021 ) Soil Type Soft soil Stiff soil Hard soli (Meghdad Hajibabaee, Amini-hosseini, & Reza, 2014 and Vicente, Ferreira, & Maio, 2014 ). Table 2 Systematic Earthquake Vulnerability Indicators Indicators Vulnerability Level Supportive literature High Medium Low Distance to open spaces > 300 m 200–300 m I km 500–999 m I km 500–999 m 1500 m 1000–1500 m < 1000 m (Ahasan, Alam, Chakraborty, & Hossain, 2020 ; (Alam & Haque, 2021 ) Table 3 Structural Earthquake Vulnerability Indicators Indicators Vulnerability Level Supportive literature High Medium Low Building with poor infrastructure (%) > 50 25–50 50 25–50 < 25 (Alam & Haque, 2018 ; Rahman et al., 2015 ). Mean Road Width 15 ft (Alam & Haque, 2021 ; Ghajari et al., 2017 ; Martins, 2018 ; Armaş, 2012 ) Building with irregular shapes (%) > 15 10–15 3 stories 2 stories 1 story (Alizadeh et al., 2018 ; Nath, Adhikari, Devaraj, & Maiti, 2015 ) Building density (acre) > 15 10–15 20 10–20 15 10–15 20 10–20 < 10 (Alam & Haque, 2018 ; Ozmen et al., 2014 ; Alam & Haque, 2021 ) Table 4 Socio Economic Earthquake Vulnerability Indicators Indicators Vulnerability Level Supportive literature High Medium Low Population above 60 years of age (%) > 06 03–06 10 06–10 45 30–45 60 40–60 < 40 (Vicente et al., 2014 ; Alam & Haque, 2021 ; Household income (Average) Below poverty line At poverty threshold Above poverty line (Alizadeh et al., 2018 ; Nath; Adhikari, Devaraj, & Maiti, 2015 ; Rahman et al., 2015 ) Family Size (Average) > 10 5–10 160 pop/acre 100–160 pop/acre 40 20–40 < 10 (Alam & Haque, 2018 ; Alam & Haque, 2021 ; (Ozmen et al., 2014 ) 2.3. Methodology Earthquake risk assessment is the function of three components including earthquake hazard, exposure and earthquake vulnerability. Multi-Criteria Decision Making (MCDM) process is used for earthquake risk assessment in this study. Weighted Linear Combination and Analytical Hierarchy Process are widely used (MCDM) techniques by researchers to assess earthquake vulnerability and risk (Armaş, 2012 ; Alam & Haque, 2018 ; Alizadeh et al., 2018 ; Alam & Haque, 2021 ). This study utilizes both AHP & WLC methods to identify earthquake risk based on selected indicators. Analytical Hierarchal process involves the following steps, making a hierarchical process for indicators; designing a reciprocal matrix of the factors from pairwise comparison. Similarly, for the computation of eigen-vector and eigen-value, (Yoram, 1980) developed a nine-point scale to identify the weight of indicators, and testing the consistency of the decisions through following equations. $$CI=\frac{\text{m}\text{a}\text{x} -n}{\text{n}-1}$$ 1 $$CR=\frac{CI}{RI}$$ 2 Were, λ max is the principle or maximum eigenvalue of the matrix and RI represents the inconsistency random index which depends on the numbers of indicators of earthquake risk assessment. Table 5 Pairwise comparison and preference scale in AHP (Yoram, 1980) Relative intensity of Importance Decreasing Equal Importance Relative Intensity of Importance Increasing 1 Weighted Linear combination method is used for combining indicators by applying a weight to each indicator from the AHP model, WLC is an extensively used applied MCDM technique. using a weighted overlay analysis in Arc-GIS all the weighted layers of hazard and vulnerability indicators and their sub-components are combined through the following equation as; $$W={\sum }_{J=1}^{n}\left(wi*xi\right)$$ 3 In Eq. 3 , W represents the index weight score of each Zone in the map, W i represents the weight of each indicator, X i and n represents the number of indicators. Results 3.1 Hazard Analysis we examined the overall situation of Quetta area by preparing three maps related to earthquakes in order to feed the risk analysis. 1) A Surface PGA map is produced, considering the local site response with the ground level motion in Quetta city, and 2) the soil map is prepared to identify the nature of hard, swift and soft soil and 3) faults line map is prepared from USGS to identify that which Zones are most closed to fault lines. Finally, the three (Fault line, PGA, Soil) maps are overlaid to produce earthquake hazard map as show in Fig. 3 . Since the identification of seismic sources and their potential is much needed for the overall risk quantification of earthquake hazards (Comercial & Pesqueros, 2014 ). Therefore, areas sources and fault lines of the seismicity are considered for this purpose. Current PGA values are taken from the USGS database to identify the ground motion in the study area. soil type is divided into three categories, hard, swift and soft. Zones with the soft soil are considered highly vulnerable. The soft and thick soil produces more earthquake implications and shaking. Swift soil is neither so hard nor soft in nature. It is the medium category of soil having moderate level of vulnerability. Similarly, Zones with rocks are considered low vulnerable. The soil data layer is taken from the Geological Survey of Pakistan. The PGA value of Quetta city varies from 0.311 to 0.481 g. This range is declared a severe perceived shaking range by USGS, Instrumental Intensity Scale (Bendito et al., 2014 ). The highest PGA values are observed in the northern and western parts of Quetta city, which can cause huge destruction in the future. The fault lines are also considered one of the important dimensions of earthquake hazard assessment. The two fault lines observed in the study area passing though the different Zones. The closest the Zones with fault lines are considered highly vulnerable. Based on the results of hazard analysis, four out of thirteen Zones (Hazargunji, Samungli, Hazara Town, and Quetta north) are highly hazardous, and four Zones (Quetta east, Kharot Abad, Salim town, and Jinnah town) are found as medium hazardous, and five Zones (Satellite Town, Pashtoon Abad, Saryab, Centrum of the city, and Quetta Cantt) are found low hazardous areas in term of earthquake hazard in Quetta city as shown in Fig. 3 . additionally, Fig. 4 . shows the influence of each geological dimension on a scale of 0–1 on earthquake hazards. The highest influence of soil type (0.43) is observed, followed by Peak Ground acceleration (0.34). Whereas, fault lines (0.22) have the least influence among the three geological dimensions used in the analysis. The energy quickly passes with a low level of amplitude through hard soil and thus causes minimum destruction to the buildings on the surface. But the softy soil increases the amplitude and slows down the energy of the motion of a quack, which is the main cause of earthquake destruction to the infrastructure. 3.2 Exposure Analysis (Population Density) exposure is taken as the second component of earthquake risk assessment. The term exposure in disaster management identifies the number of assets (e.g., physical, environmental, economic, cultural, historical, social, etc.) and the number of people exposed to a known hazards (Kamranzad et al., 2020 ). Exposure can be assessed based on estimated and observed data. we used the observed (Census) data to identify the human exposure to earthquake hazards in the study area. Over the past 5 years, the population of Quetta city has increased from 1.04 million to 1.16 million people. the result in Fig. 5 shows that; four out of thirteen Zones (Centrum of the city, Pashtoon Abad, Kharot Abad, and Salim Town) are highly densely populated Zones and four Zones (Saryab, Satellite Town, Hazar Town, AND Jinnah Town) have a medium level of population density. Five out of thirteen Zones (Hazargunji, Quetta, East, Quetta North, Quetta Cantt, and Samungli) have a low level of population density. 3.3. Vulnerability Analysis Vulnerability assessment is based on three main components including systematic, structural and socio-economic. Each component is analyzed as followed. Furthermore, within the components, indicator wise influence is also being analyzed to identify the most significant indicator within the components. The Geometric mean is used to identify the distances from the center of each Zone in the Arc-GIS environment to measure the systematic vulnerability of the study area. The four main indicators used for systematic vulnerability are open spaces, emergency centers, hospitals, and fire services. Results of Fig. 10 (earthquake vulnerability map) show that 4 out of 13 Zones (Hazar-Gungi, Quetta East, Quetta North, and Samungli) are highly vulnerable to long distances among Zones and facilities available within the city. Similarly, 4 Zones of Quetta city (Satellite Town, Saryab, Hazara Town, and Quetta Cantt) fall in the medium systematic earthquake vulnerable Zones. only five Zones have low systematic earthquake vulnerability. These Zones have close spatial links with four major facilities. Indicator-wise assessment is carried out on a scale of 0–1 of systematic earthquake vulnerability of Quetta city shown in Fig. 7 . Most of the Zones in Quetta city have medium to high systematic vulnerability due to their long spatial links from health care centers (0.29), fire services (0.28), open spaces (0.25), and emergency centers (0.18) respectively. During an earthquake, hospital is the primary and significant facility for emergency response for an affected community. Only ten Government hospitals are available in Quetta city. However, most of these hospitals are spatially located in the middle and core areas of the city, while 4 Zones are outside of these hospitals’ Service Areas. The earthquake can also damage and destroy the gas lines, power stations of electricity, or other causative fire sources outside or inside of a building, which can cause the threat of fire hazards in the community after an earthquake disaster (Alam & Haque, 2021 ). However, there are only 4 fire service stations located in the middle part of the city area of 1.16 million populations. These four service stations are located only in two Zones (Centrum of Quetta and Jinnah Town). The indicators of structural earthquake vulnerability related to the built-up environment factors such as bridges, buildings, roads, etc. indicators related to structural earthquake vulnerability have a potential influence on earthquake damage and vulnerability of a community prone to earthquake hazards (Alam & Haque, 2021 ). Nine significant structural indicators are selected to assess the structural earthquake vulnerability. Results of the structural earthquake vulnerability show that; five Zones among the thirteen are highly vulnerable in terms of structural earthquake vulnerability. These include (Hazraganji, Quetta East, Saryab, Pashtoon Abad, and Kharotabad). Similarly, Samungli, Quetta North, Centrum of Quetta, and Haazar Town have medium structural earthquake vulnerability. Only four Zones have low structural earthquake vulnerability as shown in Fig. 10 . Indicator-wise assessment is carried out on a scale of 0–1 of structural earthquake vulnerability of Quetta city shown in Fig. 7 . Results of the analysis of the overall structural earthquake vulnerability show that high pounding possibility (0.17), building with poor condition (0.17), road width (0.16), building with irregular shapes (0.13) possibility of overhanging (0.12), Building Density (0.09), building with flexible roofs (0.07), and building stories (0.07) respectively are the significant contributing factors of structural earthquake vulnerability in Quetta city. For the socio-economic vulnerability analysis, eight indicators such as the children’s population, population above 60 years of age, women’s population, dependent population, illiteracy rate, family members, family, income, and population density. Results of the analysis show that 6 out of 13 Zones of Quetta city are highly vulnerable to earthquake hazards in terms of socio-economic vulnerability as shown in Fig. 10 . Whereas three out of thirteen Zones have a medium level of vulnerability and only 4 Zones (Quetta Cantt, Centrum of Quetta, Jinnah Town, and Satellite Town) are the low vulnerable Zones in terms of socio-economic vulnerability. The indicators wise assessment of the Zones of Quetta city for socio-economic vulnerability is shown in Fig. 8 . The study area is mainly vulnerable due to the high percentage of children population (0.21), elderly population (0.20), a high percentage of women (0.13), low family income (0.12), population density (0.10), dependent population, and illiteracy rate with (0.07), and family size with (0.06) are the aforementioned parameters which makes the city highly socio-economically vulnerable. The composite earthquake vulnerability is based on 24 important indicators jointly taken from four components (socio-economic, structural, geological, and systematic) of earthquake vulnerability. The combined result of the composite earthquake vulnerability shows that 7 out of thirteen Zones of the Quetta city are highly vulnerable as shown in Figure. 10. Four Zones have a medium level of vulnerability and only two Zones are considered low earthquake vulnerable Zones in the study area. Based on the results, it is found that distance to hospitals (0.24), distance to fire services (0.22), distance to open spaces (0.21), aged population (0.21), children population (0.20), pounding (0.17), low road width (0.14), overhanging (0.13), women population (0.13), distance to emergency centers (0.13), building with poor condition (0.12), and family income (0.12) respectively are the significant and topmost indicators that make the Quetta city highly vulnerable to earthquake hazard. Whereas building with irregular shapes (0.11), and population density (0.10) have a medium-level influence on earthquake vulnerability in the study area. Similarly, illiteracy rate (0.07), dependent population (0.07), building stories (0.07), building with flexible roofs (0.07), family size (0.06), and building density (0.06) have to somewhat low influence on overall earthquake vulnerability in the study area as shown in Figure. 09. Source: Jamal-uddin et al. 2023 3.4. Earthquake Risk Map After analyzing the components of risk assessment such as vulnerability, hazard, and exposure, it is important to classify these maps and combine their values in order to make the final earthquake risk map for the study area. a composite risk index is used to combining all the components of vulnerability, hazard, and exposure as in Eq. 4. Risk index = W vul *Y vul + W haz *Y haz + W pop_d *Y pop_d ___________________ (4). Here Wi and Y i denoted the overall weights and index values of vulnerability, hazard, and population density, respectively. Based on the results of the earthquake risk map shown in Fig. 11 ; five out of thirteen Zones (Hazargunji, Quetta east, Kharot Abad, Samungli, and Quetta North) are considered high earthquake risk Zones. Four Zones (Centrum of Quetta, Pashtoon Abad, Saryab, and Hazara Town) are considered medium earthquake risk Zones. Only three Zones (Quetta Cantt, Satellite Town, and Jinnah Town) have a low level of earthquake risk. This research study has significant implications for urban planners and provides a risk reduction platform to reduce future earthquake losses and make Quetta city more resilient and sustainable. 3.5. Conclusion This study has used multi-criteria analysis for the earthquake risk assessment of Quetta city, using both primary and secondary data and information. Risk mapping is carried out which includes the hazard, exposure and vulnerability analysis based on the selected parameters/indicators and prepared their maps respectively, for risk map, all the maps are overlaid in AR-GIS. The results reveal that in Quetta city, five out thirteen zones are at highly risk, four zones with medium risk and three zones with low risk for earthquake hazards. From the results, it can be inferred that both structural and nonstructural measures are inevitable. Steps on war putting are required for the safety of the human and infrastructure otherwise the recurrence would be more disastrous and cruel in nature. The paper recommends that earthquake risk map of Quetta may be used for risk communication, decision making, land use planning, development of critical infrastructure. The paper further recommends that this map may be utilized as guide for emergency response systems, building codes implementation and preparedness of the community. 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Geological Society, London, Special Publications , SP483.16. https://doi.org/10.1144/sp483.16 Barkat A, Javed F, Joe Tan Y, Ali A, Tahir Javed M, Ahmad N, Awais M, Shah MA, Iqbal T (2022) 2019 Mw 5.9 Mirpur, Pakistan Earthquake: Insights from Integrating Geodetic, Seismic, and Field Observations. Seismol Res Lett XX 1–12. 10.1785/0220210322 Bilham R (2009) The seismic future of cities. Bull Earthq Eng 7(4). 10.1007/s10518-009-9147-0 Ceferino L, Mitrani-Reiser J, Kiremidjian A, Deierlein G, Bambarén C (2020) Effective plans for hospital system response to earthquake emergencies. Nat Commun 11(1):1–12. https://doi.org/10.1038/s41467-020-18072-w Comercial B, Pesqueros DEP (2014) No 主観的健康感を中心とした在宅高齢者における 健康関連指標に関する共分散構造分析Title. 11(2010), 28–34 Cutter SL, Boruff BJ, Shirley WL (2003) Social vulnerability to environmental hazards. 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In Human Casualties in Earthquakes, 13–24 Ghajari E, Alesheikh A, Modiri M, Hosnavi R, Abbasi M (2017) Spatial Modelling of Urban Physical Vulnerability to Explosion Hazards Using GIS and Fuzzy MCDA, Sustainability 2017, 9 (7), 1274; https://doi.org/10.3390/su9071274 Halvorson SJ, Hamilton JP (2010) In the aftermath of the Qa’yamat: The Kashmir earthquake disaster in northern Pakistan. Disasters 34(1):184–204. https://doi.org/10.1111/j.1467-7717.2009.01124.x Hazard E (2012) Earthquake Risk Assessment of Quetta. 02(September) Jena R, Pradhan B (2020) Integrated ANN-cross-validation and AHP-TOPSIS model to improve earthquake risk assessment. Int J Disaster Risk Reduct 50:101723. https://doi.org/10.1016/j.ijdrr.2020.101723 Khan A, Pan X, Najeeb U, Tan DKY, Fahad S, Zahoor R, Luo H (2018) Coping with drought: Stress and adaptive mechanisms, and management through cultural and molecular alternatives in cotton as vital constituents for plant stress resilience and fitness. Biol Res 51(1). https://doi.org/10.1186/s40659-018-0198-z Kuddus MA, Tynan E, McBryde E (2020) Urbanization: A problem for the rich and the poor? Public Health Rev 41(1):1–4. https://doi.org/10.1186/s40985-019-0116-0 Kamranzad F, Memarian H, Zare M (2020) کامران زاد.Pdf. 1 Martins L (2018) Earthquake Damage and Loss Assessment of Reinforced Concrete Buildings. PhD Dissertation, University of Porto Meghdad H, Kambod A (2014) Second European conference on Earthquake Engineering and Seismology, Istanbol from 25-09-2014 Mehdi B, Mohammed N, Ahmed M, Mohamed B, Mounir A, Belkacem N, Nabila G, Dalila A, Mounir N, Nourredine M, Omar A (2018) Seismic vulnerability assessment at urban scale: Case of Algerian buildingsInternational Journal of Disaster Risk Reduction. V3. Press 555–575 Nath SK, Adhikari MD, Devaraj N, Maiti SK (2015) Seismic vulnerability and risk assessment of Kolkata City, India. Nat Hazards Earth Syst Sci 15(6):1103–1121. https://doi.org/10.5194/nhess-15-1103-2015 Ozmen HB, Inel M, MERAL E (2014) Evaluation of the main parameters affecting seismic performance of the RC buildings. Sadhana - Academy Proceedings in Engineering Sciences, 39(2), 437–450. https://doi.org/10.1007/s12046-014-0235-8 Page-Tan C D. tools for assessing disaster risk reduction: A. analysis of, Reduction, spatial disaster risk reduction datasets. T. 2022 U. N. G. A. R. on D. R., Reduction (GAR (2022) U.N. Office for Disaster Risk. (20189) Rahman N, Ansary MA, Islam I (2015) GIS based mapping of vulnerability to earthquake and fire hazard in Dhaka city, Bangladesh. Int J Disaster Risk Reduct 13:291–300. https://doi.org/10.1016/j.ijdrr.2015.07.003 Rehman SU, Lindholm C, Ahmed N, Rafi Z (2014a) Probabilistic seismic hazard analysis for the city of Quetta. Pakistan Acta Geophys 62(4):737–761. https://doi.org/10.2478/s11600-013-0186-1 Rehman SU, Lindholm C, Ahmed N, Rafi Z (2014b) Probabilistic seismic hazard analysis for the city of Quetta. Pakistan Acta Geophys 62(4):737–761. https://doi.org/10.2478/s11600-013-0186-1 Rezaie F, Panahi M (2015) GIS modeling of seismic vulnerability of residential fabrics considering geotechnical, structural, social and physical distance indicators in Tehran using multi-criteria decision-making techniques. Nat Hazards Earth Syst Sci 15(3):461–474. https://doi.org/10.5194/nhess-15-461-2015 Sarkar S (2017) Estimation of Seismic Hazard Using PSHA in and around National Capital Region (NCR) of India. February 2018. https://doi.org/10.5923/j.geo.20170704.01 Shah MA (2012) Deterministic Seismic Hazard Assessment of Quetta, Pakistan. 15th World Conference on Earthquake Engineering, Lisbon Portugal Tobin GA (1999) Sustainability and community resilience: The holy grail of hazards planning? Environ Hazards 1(1):13–25. https://doi.org/10.3763/ehaz.1999.0103 UNISDR (The United Nations International Strategy for Disaster Reduction) (2013) From Shared Risk to Shared Value –The Business Case for Disaster Risk Reduction. In Global Assessment Report on Disaster Risk Reduction Vicente R, Ferreira T, Maio R (2014) Seismic Risk at the Urban Scale: Assessment, Mapping and Planning. Procedia Econ Finance 18(September):71–80. https://doi.org/10.1016/s2212-5671(14)00915-0 Yoram Wind TL, Saaty (1980) Marketing Applications of the Analytic Hierarchy Process. Manage Sci 26(7):641–658. https://doi.org/10.1287/mnsc.26.7.641 Zebardast E (2013) Constructing a social vulnerability index to earthquake hazards using a hybrid factor analysis and analytic network process (F’ANP) model. Nat Hazards 65(3):1331–1359. https://doi.org/10.1007/s11069-012-0412- Additional Declarations No competing interests reported. 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Not","lastName":"applicable","suffix":""},{"id":318656106,"identity":"0e2b1362-0c42-4448-8877-3f1789afc45e","order_by":5,"name":"Shabana Faiz","email":"","orcid":"","institution":"University of Balochistan","correspondingAuthor":false,"prefix":"","firstName":"Shabana","middleName":"","lastName":"Faiz","suffix":""}],"badges":[],"createdAt":"2024-06-11 02:41:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4560765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4560765/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59568976,"identity":"6789cb53-f10d-413a-9dd2-473422bd6740","added_by":"auto","created_at":"2024-07-03 09:42:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50491,"visible":true,"origin":"","legend":"\u003cp\u003eInstrumental and historical seismicity in and around Quetta city adopted from Pakistan-US Science and Technology Cooperation Program (2012).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/6e64b6352f4dfd74ab2698ae.jpg"},{"id":59568970,"identity":"2116e711-0b87-40ab-90dc-b74a6b0cbd55","added_by":"auto","created_at":"2024-07-03 09:42:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":120258,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area Map\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/b28aaf2e5f94d0edda17a116.jpg"},{"id":59568975,"identity":"c472ea25-3b90-495c-9de8-e199503db7a8","added_by":"auto","created_at":"2024-07-03 09:42:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144029,"visible":true,"origin":"","legend":"\u003cp\u003eEarthquake Hazard Map\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/deb4b0c341ceb09da84f1942.jpg"},{"id":59569556,"identity":"fc0b9a03-1998-4c69-9382-9b51a040e0c5","added_by":"auto","created_at":"2024-07-03 09:50:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":200763,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of Geological Indicators on Earthquake Hazard\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/103556f88d3921bebc34ca62.jpg"},{"id":59570373,"identity":"8cf5ded5-b81f-446b-9ec5-b58d1522f1b7","added_by":"auto","created_at":"2024-07-03 09:58:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84860,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation Density\u003cem\u003e \u003c/em\u003eMap\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/10d9ce68600b0743b25dd0ca.jpg"},{"id":59568978,"identity":"8e7c0f17-f772-41bd-a6e0-e04cea727c91","added_by":"auto","created_at":"2024-07-03 09:42:04","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":268227,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of Systematic Indicators on Earthquake Vulnerability\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/40d5bfaa1e7b3561a552178e.jpg"},{"id":59570859,"identity":"a1c2a870-a130-4f24-bbc0-a1f429028164","added_by":"auto","created_at":"2024-07-03 10:06:03","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":327533,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of Structural Indicators on Earthquake Vulnerability\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/66a20dbc9515d0d51a3c9630.jpg"},{"id":59569557,"identity":"c80dabfc-6b0f-4adf-ba27-20fd87f271f1","added_by":"auto","created_at":"2024-07-03 09:50:03","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":282088,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of Socio-economic Indicators on Earthquake Vulnerability\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/e6958a9d6b12d386afc085dd.jpg"},{"id":59568989,"identity":"99d082ff-2548-4fa1-b090-6d54bdb9ed3a","added_by":"auto","created_at":"2024-07-03 09:42:04","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":209516,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInfluence of Indicators on Overall Earthquake Vulnerability\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/c6396912e94eae0746f255f1.jpg"},{"id":59568979,"identity":"8de6f01b-ab03-4fb8-aa29-f6414beb3b8b","added_by":"auto","created_at":"2024-07-03 09:42:04","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":171661,"visible":true,"origin":"","legend":"\u003cp\u003eEarthquake Vulnerability Map\u003c/p\u003e\n\u003cp\u003eSource: Jamal-uddin et al. 2023\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/a27efe8c7d7aa418ea81f0d9.jpg"},{"id":59568977,"identity":"cbfdaf10-b84f-4db7-96ea-a20c7b0c0d58","added_by":"auto","created_at":"2024-07-03 09:42:03","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":96967,"visible":true,"origin":"","legend":"\u003cp\u003eEarthquake Risk Map\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/f79717bf6cfe2ca36c380a80.jpg"},{"id":60763710,"identity":"22de1288-01eb-462b-ac3d-39bbe6db7ffd","added_by":"auto","created_at":"2024-07-21 09:16:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2591305,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4560765/v1/d7a32ba6-25d2-456c-9dca-cea9f0b6b500.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Earthquake Risk Assesment of Urban Quetta, using Multi-criteria Anlaysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFrom 2000\u0026ndash;2019, natural disasters have killed 1.23\u0026nbsp;million people, 4.2\u0026nbsp;billion got affected along with economic damage of 2.97 trillion US\u003cspan\u003e$\u003c/span\u003e around the globe (Page, T. et al., 2022). Economic, environmental and social costs associated with such disasters are being observed and recorded in the recant major earthquakes occurred in Indonesia in 2004, Pakistan 2005, China 2008, Haiti 2010, Japan 2011, Nepal 2015, Ecuador 2016 and Turkey 2023. These events have exerted pressure on nations to develop appropriate cost-effective earthquake risk assessment tools particularly in seismic prone developing countries (Khan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u0026amp; Mehdi Boukri et al., 2018). CRED database for disasters indicates that the reported number of natural disasters have been increased enormously from the beginning of 19th century till date. (Sarkar, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A natural hazard only becomes a disaster when it affects the exposed and vulnerable population and infrastructures (Adger, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Natural hazards like earthquakes, droughts and hurricanes are unpreventable, but their damaging impacts can be mitigated and minimized through proper planning and implementation (Rehman et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e). Earthquakes are the most lethal type of natural disasters amongst all others especially in urban area due to its unpredictable nature (Ainuddin and Routray 2012; Bilham \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Aldrich, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At the start of the 20th Century, 2% two percent people lived in just 14 mega-cities of the world. Currently this proportion is increased to 20% twenty percent and it will probably increase to 40% forty percent by the year 2030 (Ceferino et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is evident that cities will face problems being turned into mega-polis from metro-polis due to urbanization which is inevitable process. (Alam \u0026amp; Haque, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the increasing pace of urbanization process, cities in developing countries are more likely to be exposed to physical, economic and human losses due to the increased risk to natural hazards particularly earthquakes (Kuddus et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Seismic Hazards and Pakistan","content":"\u003cp\u003eSeismic hazards and their associated risks are on rise globally and becoming more disastrous in countries with poor policy measures, ignorance and corrupt practices in construction industry (Bilham, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Armas et al., 2017). According to the EM-DAT, an average of 27,000 people are killed per year due to earthquakes. Every day, almost 4000 earthquakes of different scales occur around the planet (Guha-Sapir \u0026amp; Vos, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The continent of Asia has particularly remained a permanent victim of earthquakes of different scales (Bilham, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Aldrich, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The historical data shows that earthquake occurrence ratio between Asia and the rest of the world is 55:45 (UNISDR, 2013). Damages due to earthquakes in Asia may be linked to poverty, increasing trends in urbanization, poor construction practices, and lack of policy implementation (Aitsi-Selmi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bilham \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, Dolce et al., (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) argues that risk assessment of seismic hazards can support policymakers in designing risk mitigation measures that can include enforcement of building codes, emergency evacuation plans, retrofitting activities, and development of insurance pools. Earthquake hazard and vulnerability assessment aim to provide a reliable prediction of expected physical damage as well as social and economic losses due to potential earthquake events (Alizadeh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This can enhance the potential of urban planning in the context of physical infrastructures and hazard mitigation efforts (Tobin, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Boukri et al., 2018). Pakistan is no exception to it, which is geographically prone to earthquake hazards and subjected to major earthquakes in the past with various ranks of vulnerabilities. (Halverson 2010; Bilham, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Five major earthquakes with the range of 6.5 to 8.0 magnitudes occurred with potential human and economic losses from 1900 to 2008 (Rehman et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e). The 2019, Mirpore earthquake raised many questions for policymakers and local administration, where, almost all the infrastructures including housing, roads, overflies and water reservoirs were completely collapsed. More than 40 people were killed, 400 people were injured and thousands of families were displaced (Barkat et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Seismically, Pakistan is among the most active regions for earthquakes on the globe due to the existing collision boundaries of Eurasian, Arabian and Indian plates. The collision of the plate boundary in the north is of convergent nature as Indian plate is sub-ducting beneath the Eurasian plate, with 36\u0026ndash;42 mm/year (Chen et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In the western part of the country, the nature of the plate boundary is transform marked by the well-known active Chaman fault as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Arabian plate is converging at the rate of 28\u0026ndash;33 mm/year with the Eurasian plate in the south along with subduction zone of Makran (Apel et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In the recent past, major catastrophic earthquake events occurred due to the collision of these plate boundaries, which include Kangra (1905, M\u0026thinsp;~\u0026thinsp;8.0); Quetta (1935, M\u0026thinsp;~\u0026thinsp;7.6); Makran Tsunami (1945, M\u0026thinsp;~\u0026thinsp;8.3); Hunza (1974, M\u0026thinsp;~\u0026thinsp;6.2); Chamman (1992, M\u0026thinsp;~\u0026thinsp;6.2); Kashmir (2005, M\u0026thinsp;~\u0026thinsp;7.6); Ziarat (2008, M\u0026thinsp;~\u0026thinsp;6.4); Hindukush (2015, Mm\u0026thinsp;~\u0026thinsp;7.5); and Hurnai (2021, M\u0026thinsp;~\u0026thinsp;5.9) earthquakes.\u003c/p\u003e \u003cp\u003eEarthquake risk assessment is one of the best efforts to address the adverse impacts of seismic hazard in a particular area, especially in urban areas where population density is high. In the city of Quetta, three earlier studies related to earthquake risk assessment were conducted. The first study was conducted by (Hazard, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In which the city was divided into thirteen zones based on socio-economic conditions, population, building typology and soil type. Three earthquake scenarios with magnitudes 6.5, 6.9, and 7.3 with epicenters that were 105, 21 and 66 km away from the center of the city of Quetta, respectively were used in the damage and loss assessment. While the second study was conducted by (Shah, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), in which probabilistic seismic hazard analysis technique was used based on area sources and augmented by line source used for the earthquake loss and damage assessment. The third study was conducted by (Rehman, Lindholm, Ahmed, \u0026amp; Rafi, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e), which was focused only on hazard assessment using deterministic seismic risk assessment (DSHA). In these three studies, many important aspects of risk assessment and indicators like evacuation roots, open spaces, fire services, healthcare services and emergency routes were not assessed, which could make the estimations process illogical in the context of the study area. In the 2nd and 3rd studies, only earthquake hazard was focused, however, hazard assessment alone would not serve the best without taking vulnerability and exposure into account to understand the overall seismicity of the city. Therefore, to overcome these issues pointed out in the above-mentioned studies, the current study has focused on a detailed seismic risk assessment approach taking care of the missing indications and components. Because a holistic risk assessment is essential to measure hazard, vulnerability and exposer to avoid the impacts of future earthquake events and guide the policy makers (Cutter et al 2010; Khan 2007). The current study can also help disaster professionals and policy makers to focus on community level risk reduction strategies, building codes enforcement, retrofitting activities and others essential measures in line with Sendai framework and sustainable development goals (SDG\u0026rsquo;S) agendas (2015\u0026ndash;2030).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e2.1\u003c/strong\u003e Selection of Study Area\u003c/h2\u003e\n \u003cp\u003eQuetta, the capital of the province is located in a very active seismic zone and frequently faced different scales of catastrophic earthquake events with various potential damages in the history. In the earthquake zonation map of Pakistan, Quetta is situated in the first zone, which is very high active zone in the context of earthquake (Ainuddin \u0026amp; Routray, 2012). The entire city was demolished in the (1935) Quetta earthquake with 7.6 magnitudes on reactor scale. Currently Quetta city has an area of 176 km\u003csup\u003e2\u003c/sup\u003e. Apart from the occurrence of an earthquake, some of the other hazards likes subsidence, surface fault, landslide, liquefaction and secondary hazard like fire following the earthquake are possible to occur. Therefore, it is important to see each vulnerable aspect of earthquake hazard. The study area is divvied into thirteen Zones based on their socioeconomic and demographic profile as shown in given in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Data Collection Methods\u003c/h2\u003e\n \u003cp\u003eData was collected from both primary and secondary sources. Primary data was collected through questionnaire survey with 400 sample respondents to acquire information using proportionate allocation method. While secondary data was downloaded from the United States Geological Survey (USGS) to get the Peak Ground Acceleration (PGA) values of the study area. Geological Survey of Pakistan (GSP) provided the geological map and soil data of the study area. Population data was taken from Pakistan Bureau of Statistics (PBS) for population density map. Similarly, other necessary information was taken from literature and previous studies. Furthermore. point feature is used to collect the data on systemic vulnerability.\u003c/p\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Sample Size Selection\u003c/h2\u003e\n \u003cp\u003eQuetta city was divided into thirteen zones based on their socioeconomic and demographic information. Based on the Arkin and Colton formula (1963) of sample size, a total of 400 sample respondents were investigated for the primary household survey. Stratified random sampling technique is employed using proportionate allocation method due to the heterogeneity of the area in terms of population in the thirteen zones. The sample size formula calculation is given below.\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\text{n}=\\frac{\\text{N}{\\text{Z}}^{2}\\times \\text{P}\\times (1 -\\text{P})}{\\text{N}{\\text{e}}^{2}+{\\{\\text{Z}}^{2}\\times \\text{P}\\times (1 -\\text{P})\\}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\text{n}=400$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eProportionate Allocation formula for each zone\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$${\\text{n}}_{\\text{k}}=\\frac{{\\text{N}}_{\\text{k}}}{\\text{N}} \\times \\text{n}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere,\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;Total number of Households\u003c/p\u003e\n \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;Confidence Interval (95%)\u0026thinsp;=\u0026thinsp;1.96\u003c/p\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;Degree of Variation\u0026thinsp;=\u0026thinsp;50% = 0.50\u003c/p\u003e\n \u003cp\u003eE\u0026thinsp;=\u0026thinsp;Margin of error\u0026thinsp;=\u0026thinsp;5% = 0.05\u003c/p\u003e\n \u003cp\u003eData was analyzed through Statistical Package for Social Sciences (SPSS), Excel, and Geographic Information System (GIS). Data related to the socio-economic and structural components are performed in SPSS software and then transferred into the GIS environment to generate required maps. Whereas indicators related to hazard assessment like Peak Ground acceleration (PGA), Fault Lines (FL) and Soil Type (ST) are performed in Arc-GIS to produce the expected maps and results. Analytical Hierarchal Process (AHP) and Weighted Linear Combination (WLC) methods are used to identify earthquake hazard, vulnerability and risk maps based on developed indicators.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2. \u003cstrong\u003eSelection of Indicators/Parameters\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eThe parameters/indicators for this research are taken from the available literature. 24 seismic risk indicators have been selected for the risk assessment. Notably, these indicators are adopted from the studies of (Cutter, Boruff, \u0026amp; Shirley, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ainuddin \u0026amp; Routray, 2012 and Alam \u0026amp; Haque,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). These indicators with supportive literature are shown in Table, 1,2,3 and 4 respectively.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003cbr\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEarthquake Hazard indicators\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVulnerability Level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSupportive literature\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeak Ground Acceleration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.351\u0026ndash;0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.311\u0026ndash;0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; (Jena \u0026amp; Pradhan, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e and (Rezaie \u0026amp; Panahi, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFaults Line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1000 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u0026ndash;1500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Jena \u0026amp; Pradhan, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoft soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStiff soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHard soli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Meghdad Hajibabaee, Amini-hosseini, \u0026amp; Reza, 2014 and Vicente, Ferreira, \u0026amp; Maio, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003cbr\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSystematic Earthquake Vulnerability Indicators\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVulnerability Level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSupportive literature\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to open spaces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 300 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u0026ndash;300 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;200 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Nath, Adhikari, Devaraj, \u0026amp; Maiti, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rezaie \u0026amp; Panahi, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to Healthcare center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; I km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u0026ndash;999 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Jena \u0026amp; Pradhan, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to Emergency Center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; I km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u0026ndash;999 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Meghdad Hajibabaee, Amini-hosseini, \u0026amp; Reza, 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to Fire Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 1500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u0026ndash;1500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1000 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Ahasan, Alam, Chakraborty, \u0026amp; Hossain, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; (Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003cbr\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStructural Earthquake Vulnerability Indicators\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVulnerability Level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSupportive literature\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilding with poor infrastructure (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Ghajari, Alesheikh, Modiri, Hosnavi, \u0026amp; Abbasi, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ainuddin \u0026amp; Routray, \u003cspan class=\"CitationRef\"\u003e2012b\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilding with flexible roof (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rahman et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Road Width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;9 ft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 ft-15 ft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;15 ft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ghajari et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Martins, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Armaş, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilding with irregular shapes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Vicente et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStories of the building\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3 stories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 stories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 story\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Alizadeh et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nath, Adhikari, Devaraj, \u0026amp; Maiti, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilding density (acre)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Armaş, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Jena \u0026amp; Pradhan, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Martins, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilding Age (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Nath et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zebardast, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePossibility of pounding (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; (Ozmen et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeavy overhanging (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ozmen et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSocio Economic Earthquake Vulnerability Indicators\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eVulnerability Level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSupportive literature\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation above 60 years of age (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03\u0026ndash;06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Cutter \u0026amp; Finch, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Vicente et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ainuddin \u0026amp; Routray, \u003cspan class=\"CitationRef\"\u003e2012b\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChildren below 15 years of age (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rahman et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zebardast, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale population (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ghajari et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Martins, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Armaş, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Armaş, Toma-Danila, Ionescu, \u0026amp; Gavriş, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliteracy rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Vicente et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold income (Average)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow poverty line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt poverty threshold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove poverty line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Alizadeh et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nath; Adhikari, Devaraj, \u0026amp; Maiti, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rahman et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily Size (Average)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Armaş, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Jena \u0026amp; Pradhan, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Martins, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation density /(Acre)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;160 pop/acre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u0026ndash;160 pop/acre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;100 pop/acre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Nath et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zebardast, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Martins, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEconomically dependent families (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; (Ozmen et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Methodology\u003c/h2\u003e\n \u003cp\u003eEarthquake risk assessment is the function of three components including earthquake hazard, exposure and earthquake vulnerability. Multi-Criteria Decision Making (MCDM) process is used for earthquake risk assessment in this study. Weighted Linear Combination and Analytical Hierarchy Process are widely used (MCDM) techniques by researchers to assess earthquake vulnerability and risk (Armaş, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Alizadeh et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Alam \u0026amp; Haque, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study utilizes both AHP \u0026amp; WLC methods to identify earthquake risk based on selected indicators. Analytical Hierarchal process involves the following steps, making a hierarchical process for indicators; designing a reciprocal matrix of the factors from pairwise comparison. Similarly, for the computation of eigen-vector and eigen-value, (Yoram, 1980) developed a nine-point scale to identify the weight of indicators, and testing the consistency of the decisions through following equations.\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$CI=\\frac{\\text{m}\\text{a}\\text{x} -n}{\\text{n}-1}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$CR=\\frac{CI}{RI}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWere, \u0026lambda;\u003csub\u003emax\u003c/sub\u003e is the principle or maximum eigenvalue of the matrix and RI represents the inconsistency random index which depends on the numbers of indicators of earthquake risk assessment.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePairwise comparison and preference scale in AHP (Yoram, 1980)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRelative intensity of Importance\u003c/p\u003e\n \u003cp\u003eDecreasing\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEqual Importance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRelative Intensity of Importance Increasing\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cimg 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\u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWeighted Linear combination method is used for combining indicators by applying a weight to each indicator from the AHP model, WLC is an extensively used applied MCDM technique. using a weighted overlay analysis in Arc-GIS all the weighted layers of hazard and vulnerability indicators and their sub-components are combined through the following equation as;\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$W={\\sum }_{J=1}^{n}\\left(wi*xi\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, W represents the index weight score of each Zone in the map, W\u003csub\u003ei\u003c/sub\u003e represents the weight of each indicator, X\u003csub\u003ei\u003c/sub\u003e and n represents the number of indicators.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Hazard Analysis\u003c/h2\u003e \u003cp\u003ewe examined the overall situation of Quetta area by preparing three maps related to earthquakes in order to feed the risk analysis. 1) A Surface PGA map is produced, considering the local site response with the ground level motion in Quetta city, and 2) the soil map is prepared to identify the nature of hard, swift and soft soil and 3) faults line map is prepared from USGS to identify that which Zones are most closed to fault lines. Finally, the three (Fault line, PGA, Soil) maps are overlaid to produce earthquake hazard map as show in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Since the identification of seismic sources and their potential is much needed for the overall risk quantification of earthquake hazards (Comercial \u0026amp; Pesqueros, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, areas sources and fault lines of the seismicity are considered for this purpose. Current PGA values are taken from the USGS database to identify the ground motion in the study area. soil type is divided into three categories, hard, swift and soft. Zones with the soft soil are considered highly vulnerable. The soft and thick soil produces more earthquake implications and shaking. Swift soil is neither so hard nor soft in nature. It is the medium category of soil having moderate level of vulnerability. Similarly, Zones with rocks are considered low vulnerable. The soil data layer is taken from the Geological Survey of Pakistan. The PGA value of Quetta city varies from 0.311 to 0.481 g. This range is declared a severe perceived shaking range by USGS, Instrumental Intensity Scale (Bendito et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The highest PGA values are observed in the northern and western parts of Quetta city, which can cause huge destruction in the future. The fault lines are also considered one of the important dimensions of earthquake hazard assessment. The two fault lines observed in the study area passing though the different Zones. The closest the Zones with fault lines are considered highly vulnerable.\u003c/p\u003e \u003cp\u003eBased on the results of hazard analysis, four out of thirteen Zones (Hazargunji, Samungli, Hazara Town, and Quetta north) are highly hazardous, and four Zones (Quetta east, Kharot Abad, Salim town, and Jinnah town) are found as medium hazardous, and five Zones (Satellite Town, Pashtoon Abad, Saryab, Centrum of the city, and Quetta Cantt) are found low hazardous areas in term of earthquake hazard in Quetta city as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. shows the influence of each geological dimension on a scale of 0\u0026ndash;1 on earthquake hazards. The highest influence of soil type (0.43) is observed, followed by Peak Ground acceleration (0.34). Whereas, fault lines (0.22) have the least influence among the three geological dimensions used in the analysis. The energy quickly passes with a low level of amplitude through hard soil and thus causes minimum destruction to the buildings on the surface. But the softy soil increases the amplitude and slows down the energy of the motion of a quack, which is the main cause of earthquake destruction to the infrastructure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Exposure Analysis (Population Density)\u003c/h2\u003e \u003cp\u003eexposure is taken as the second component of earthquake risk assessment. The term exposure in disaster management identifies the number of assets (e.g., physical, environmental, economic, cultural, historical, social, etc.) and the number of people exposed to a known hazards (Kamranzad et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Exposure can be assessed based on estimated and observed data. we used the observed (Census) data to identify the human exposure to earthquake hazards in the study area. Over the past 5 years, the population of Quetta city has increased from 1.04\u0026nbsp;million to 1.16\u0026nbsp;million people. the result in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that; four out of thirteen Zones (Centrum of the city, Pashtoon Abad, Kharot Abad, and Salim Town) are highly densely populated Zones and four Zones (Saryab, Satellite Town, Hazar Town, AND Jinnah Town) have a medium level of population density. Five out of thirteen Zones (Hazargunji, Quetta, East, Quetta North, Quetta Cantt, and Samungli) have a low level of population density.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Vulnerability Analysis\u003c/h2\u003e \u003cp\u003eVulnerability assessment is based on three main components including systematic, structural and socio-economic. Each component is analyzed as followed. Furthermore, within the components, indicator wise influence is also being analyzed to identify the most significant indicator within the components.\u003c/p\u003e \u003cp\u003eThe Geometric mean is used to identify the distances from the center of each Zone in the Arc-GIS environment to measure the systematic vulnerability of the study area. The four main indicators used for systematic vulnerability are open spaces, emergency centers, hospitals, and fire services. Results of Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e (earthquake vulnerability map) show that 4 out of 13 Zones (Hazar-Gungi, Quetta East, Quetta North, and Samungli) are highly vulnerable to long distances among Zones and facilities available within the city.\u003c/p\u003e \u003cp\u003eSimilarly, 4 Zones of Quetta city (Satellite Town, Saryab, Hazara Town, and Quetta Cantt) fall in the medium systematic earthquake vulnerable Zones. only five Zones have low systematic earthquake vulnerability. These Zones have close spatial links with four major facilities. Indicator-wise assessment is carried out on a scale of 0\u0026ndash;1 of systematic earthquake vulnerability of Quetta city shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Most of the Zones in Quetta city have medium to high systematic vulnerability due to their long spatial links from health care centers (0.29), fire services (0.28), open spaces (0.25), and emergency centers (0.18) respectively. During an earthquake, hospital is the primary and significant facility for emergency response for an affected community. Only ten Government hospitals are available in Quetta city. However, most of these hospitals are spatially located in the middle and core areas of the city, while 4 Zones are outside of these hospitals\u0026rsquo; Service Areas. The earthquake can also damage and destroy the gas lines, power stations of electricity, or other causative fire sources outside or inside of a building, which can cause the threat of fire hazards in the community after an earthquake disaster (Alam \u0026amp; Haque, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, there are only 4 fire service stations located in the middle part of the city area of 1.16\u0026nbsp;million populations. These four service stations are located only in two Zones (Centrum of Quetta and Jinnah Town).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe indicators of structural earthquake vulnerability related to the built-up environment factors such as bridges, buildings, roads, etc. indicators related to structural earthquake vulnerability have a potential influence on earthquake damage and vulnerability of a community prone to earthquake hazards (Alam \u0026amp; Haque, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nine significant structural indicators are selected to assess the structural earthquake vulnerability. Results of the structural earthquake vulnerability show that; five Zones among the thirteen are highly vulnerable in terms of structural earthquake vulnerability. These include (Hazraganji, Quetta East, Saryab, Pashtoon Abad, and Kharotabad). Similarly, Samungli, Quetta North, Centrum of Quetta, and Haazar Town have medium structural earthquake vulnerability. Only four Zones have low structural earthquake vulnerability as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Indicator-wise assessment is carried out on a scale of 0\u0026ndash;1 of structural earthquake vulnerability of Quetta city shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Results of the analysis of the overall structural earthquake vulnerability show that high pounding possibility (0.17), building with poor condition (0.17), road width (0.16), building with irregular shapes (0.13) possibility of overhanging (0.12), Building Density (0.09), building with flexible roofs (0.07), and building stories (0.07) respectively are the significant contributing factors of structural earthquake vulnerability in Quetta city.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the socio-economic vulnerability analysis, eight indicators such as the children\u0026rsquo;s population, population above 60 years of age, women\u0026rsquo;s population, dependent population, illiteracy rate, family members, family, income, and population density. Results of the analysis show that 6 out of 13 Zones of Quetta city are highly vulnerable to earthquake hazards in terms of socio-economic vulnerability as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Whereas three out of thirteen Zones have a medium level of vulnerability and only 4 Zones (Quetta Cantt, Centrum of Quetta, Jinnah Town, and Satellite Town) are the low vulnerable Zones in terms of socio-economic vulnerability. The indicators wise assessment of the Zones of Quetta city for socio-economic vulnerability is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The study area is mainly vulnerable due to the high percentage of children population (0.21), elderly population (0.20), a high percentage of women (0.13), low family income (0.12), population density (0.10), dependent population, and illiteracy rate with (0.07), and family size with (0.06) are the aforementioned parameters which makes the city highly socio-economically vulnerable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe composite earthquake vulnerability is based on 24 important indicators jointly taken from four components (socio-economic, structural, geological, and systematic) of earthquake vulnerability. The combined result of the composite earthquake vulnerability shows that 7 out of thirteen Zones of the Quetta city are highly vulnerable as shown in Figure. 10. Four Zones have a medium level of vulnerability and only two Zones are considered low earthquake vulnerable Zones in the study area. Based on the results, it is found that distance to hospitals (0.24), distance to fire services (0.22), distance to open spaces (0.21), aged population (0.21), children population (0.20), pounding (0.17), low road width (0.14), overhanging (0.13), women population (0.13), distance to emergency centers (0.13), building with poor condition (0.12), and family income (0.12) respectively are the significant and topmost indicators that make the Quetta city highly vulnerable to earthquake hazard. Whereas building with irregular shapes (0.11), and population density (0.10) have a medium-level influence on earthquake vulnerability in the study area. Similarly, illiteracy rate (0.07), dependent population (0.07), building stories (0.07), building with flexible roofs (0.07), family size (0.06), and building density (0.06) have to somewhat low influence on overall earthquake vulnerability in the study area as shown in Figure. 09.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Jamal-uddin et al. 2023\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Earthquake Risk Map\u003c/h2\u003e \u003cp\u003eAfter analyzing the components of risk assessment such as vulnerability, hazard, and exposure, it is important to classify these maps and combine their values in order to make the final earthquake risk map for the study area. a composite risk index is used to combining all the components of vulnerability, hazard, and exposure as in Eq.\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eRisk index\u0026thinsp;=\u0026thinsp;W\u003csub\u003evul\u003c/sub\u003e *Y\u003csub\u003evul\u003c/sub\u003e + W\u003csub\u003ehaz\u003c/sub\u003e*Y\u003csub\u003ehaz\u003c/sub\u003e + W\u003csub\u003epop_d\u003c/sub\u003e*Y\u003csub\u003epop_d\u003c/sub\u003e ___________________ (4).\u003c/p\u003e \u003cp\u003eHere Wi and Y\u003csub\u003ei\u003c/sub\u003e denoted the overall weights and index values of vulnerability, hazard, and population density, respectively. Based on the results of the earthquake risk map shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e; five out of thirteen Zones (Hazargunji, Quetta east, Kharot Abad, Samungli, and Quetta North) are considered high earthquake risk Zones. Four Zones (Centrum of Quetta, Pashtoon Abad, Saryab, and Hazara Town) are considered medium earthquake risk Zones. Only three Zones (Quetta Cantt, Satellite Town, and Jinnah Town) have a low level of earthquake risk. This research study has significant implications for urban planners and provides a risk reduction platform to reduce future earthquake losses and make Quetta city more resilient and sustainable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Conclusion\u003c/h2\u003e \u003cp\u003eThis study has used multi-criteria analysis for the earthquake risk assessment of Quetta city, using both primary and secondary data and information. Risk mapping is carried out which includes the hazard, exposure and vulnerability analysis based on the selected parameters/indicators and prepared their maps respectively, for risk map, all the maps are overlaid in AR-GIS. The results reveal that in Quetta city, five out thirteen zones are at highly risk, four zones with medium risk and three zones with low risk for earthquake hazards. From the results, it can be inferred that both structural and nonstructural measures are inevitable. Steps on war putting are required for the safety of the human and infrastructure otherwise the recurrence would be more disastrous and cruel in nature. The paper recommends that earthquake risk map of Quetta may be used for risk communication, decision making, land use planning, development of critical infrastructure. The paper further recommends that this map may be utilized as guide for emergency response systems, building codes implementation and preparedness of the community.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1. Dr. Syed Ainuddin, Developed and written the draft paper and also incorporated suggestion given by other colleagues 2. Dr. Chamawong reviewed the initial draft of the paper 3. Prof Ariya and Prof. routray did the detailed reviewed and given inputs. 4. 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Nat Hazards 65(3):1331\u0026ndash;1359. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-012-0412-\u003c/span\u003e\u003cspan address=\"10.1007/s11069-012-0412-\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vulnerability, Hazard, Risk Assessment, Earthquakes, Balochistan","lastPublishedDoi":"10.21203/rs.3.rs-4560765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4560765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe capital of Balochistan province Quetta is surrounded by mountain ranges with many active seismic faults. The province is subjected to many earthquakes in the past including the deadliest earthquake of 1935. The objective of this paper is to carry out earthquake risk assessment of Quetta city using multi-criteria analysis. For the purpose, the primary data was collected from 400 households using stratified random sampling technique with proportionate allocation and secondary data from USGS and Pakistan Bureau of Statistics. SPSS, GIS and Arc-GIS were used to generate the vulnerability, hazard and risk maps applying analytical hierarchy process (AHP) and weighted linear combination (WLC) methods. The results reveal that Quetta is highly vulnerable to earthquake risk in the future; its geology coupled with the human dimension indicates indicate that impacts would be more disastrous in future events. Results of the final earthquake risk map shows that five out of thirteen Zones (Hazargunji, Quetta East, Kharot Abad, Samungli, and Quetta North) are at high risk. Four Zones (Centrum of Quetta, Pashtoon Abad, Saryab, and Hazara Town) are at medium risk. Only three Zones (Quetta Cantt, Satellite Town, and Jinnah Town) are at low risk. The proposed risk map of Quetta city may be used for risk communication, decision making, land use planning and development of critical infrastructure. The paper further recommends the map to be utilized as guide for emergency response systems, and develop mitigation plans including enforcement of building codes, improve emergency response activates and educate people on earthquake preparedness.\u003c/p\u003e","manuscriptTitle":"Earthquake Risk Assesment of Urban Quetta, using Multi-criteria Anlaysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-03 09:41:58","doi":"10.21203/rs.3.rs-4560765/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b95504ea-984f-4b78-9bcc-6a805c13f1c3","owner":[],"postedDate":"July 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-21T09:08:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-03 09:41:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4560765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4560765","identity":"rs-4560765","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}