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ISSN (Print: 2357-0849, online: 2357-0857) 

Environmental Science and Sustainable Development

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Research Paper 
Received: 15 July 2023, Accepted: 13 September 2023, Published online: 30 September 2023 

DOI: 10.21625/essd.v8i4.973 

Prioritizing the Components of the Disaster Resilient System Using 
Dematel and Anp for Urban Areas 

Hajar Sarmadia1, Siavash Aghababaei2 
1Senior Lecturer in Civil Engineering, Manukau Institute of Technology - TePukenga, Auckland, New Zealand 

2Senior Transportation Engineer | Resilience, WSP, Auckland, New Zealand

Abstract 
Although many research studies have been performed on vulnerability assessment and hazard mitigation, it is 
extremely hard for communities to cope with disasters appropriately. There are many uncertainties and unanticipated 
issues in disasters; therefore, coping with disasters and emergencies is not as simple and easy as mitigation and 
preparedness activities. The concept of disaster resilience has been described as a method to overcome unforeseen 
problems. This paper aims to discover and prioritize dimensions and components of Disaster Resilient Systems (DRS) 
in urban areas using Delphi, Decision Making Trial and Evaluation Laboratory (DEMATEL), and Analytic Network 
Process (ANP). Numerous frameworks and models have been used to define relevant dimensions and components of 
DRS based on the Delphi method. The relationship between the components has been determined using DEMATEL. 
Finally, the obtained data has been applied to ANP to define a conceptual model and to obtain a weighted supermatrix 
for prioritizing the main components of DRS. The results of the study have shown that a Comprehensive Emergency 
Plan (CEP) is the most important component for earthquake resilience in urban areas. 

© 2023 The Authors. Published by IEREK Press. This is an open-access article under the CC BY license 
(https://creativecommons.org/licenses/by/4.0/). Peer review under responsibility of ESSD’s International Scientific 
Committee of Reviewers. 

Keywords 

Analytic Network Process (ANP); Delphi method; DEMATEL; Resilience; Disaster Resilient System (DRS); 

Earthquake. 

1. Introduction
Recent incidents indicate that communities and people are increasingly becoming more vulnerable to disasters, and 
the risks are globally rising (Mayunga, 2007; Ainuddin and Routray, 2012). However, risk reduction and vulnerability 
are often ignored until the occurrence of disasters (Cutter et al, 2008b). Under these circumstances in which risks and 
uncertainties are growing, the concept of resilience has been defined as the managing of disturbance, unanticipated 
events, and change (Mitchell and Harris, 2012). 

Resilience to natural hazards and disasters has been defined as the ability of communities exposed to hazards to resist, 
absorb, accommodate, and recover from the effects of a hazard in a timely and efficient manner, including through 
the preservation and restoration of its essential structures and functions (UNISDR 2009). 

While the concept of resilience has been used in science since 1625 (Alexander, 2013), in the field of ecology; it was 
first introduced by Holling (1973) and has been applied in disaster risk reduction since 2000 (Alexander, 2013). 

 In fact, after the presentation of the Hyogo Framework for Action (1995), an evident paradigm shift has occurred 
from hazard assessment to vulnerability analysis and community resilience creation in recent hazard literature 
(Ainuddin and Routray, 2012). Although there is a variety of frameworks and models in disaster resilience, there is 

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Sarmadia/ Environmental Science and Sustainable Development 

no consensus among researchers on the components and standard metrics of resilience. Moreover, the variables that 
should be utilized to measure resilience have not been determined (Ainuddin and Routray, 2012; Cutter et al., 2008b; 
Twigg, 2009). Vulnerability and resilience are indeed dynamic processes; however, they are often considered as static 
phenomena because of their measurement purposes (Cutter et al., 2008b). Recent literature (Norris et al., 2008; 
Mitchell, 2012) considers resilience as a process rather than an outcome and specifies resilience as a dynamic system. 

In addition, various definitions have been presented for disaster resilience. Mayunga (2007) has summarized some 
selected definitions of the resilience concept published in disaster and hazard literature. A number of recent definitions 
have also been provided all of which indicate there is no consensus among researchers and practitioners on a common 
definition of the concept. It is evident that the definitions are diverse, reflecting the complex nature of the concept, 
and most of them emphasize the capacity to cope with disasters, disturbances, or emergencies. In other words, 
resiliency is often the flexible capacity of a system to absorb stresses and recover rapidly from emergencies. 
Accordingly, a resilient community has been defined as one that which can cope appropriately with disasters, has the 
highest level of flexible capacity, and will not be greatly influenced by disasters. A disaster-resilient community can 
also be defined as ‘the safest possible community that we have the knowledge to design and build in a natural context’ 
(Twigg, 2009).  

Two kinds of strategies have been suggested; anticipation and resilience strategies. Anticipation strategies usually 
operate to resolve known problems, while resilience strategies better resolve unidentified problems (Wildavsky, 1988; 
Normandin et al., 2011). Resilience concentrates on community resources and methods of strengthening community 
capacities, instead of focusing on their vulnerability and deficiency (Twigg, 2009). On the other hand, vulnerability, 
as one of the most common elements of risk that is applied directly or indirectly to risk assessment methods, is often 
considered as contradictory to resilience (Twigg, 2009). However, resilience goes beyond the capacity component of 
vulnerability and the combination of these two concepts can lead to a more comprehensive understanding of disasters 
(Obrist et al., 2010). According to Godschalk (2003), a resilient city is a combination of physical systems and human 
communities. Resilient systems that can be applied to physical and social systems tend to be redundant, diverse, 
efficient, autonomous, strong, interdependent, adaptable, and collaborative (Godschalk, 2003).   

A resilient community contains a variety of complex issues. Thus, many elements are involved in resilience due to 
which its description in a clear and organized manner is almost impossible. Therefore, the Disaster Resilient System 
(DRS) is defined as a resilient system that proposes and prioritizes appropriate dimensions and components of 
resilience against contingency earthquakes in urban areas based on experts' comments using Delphi, Decision Making 
Trial and Evaluation Laboratory (DEMATEL), and Analytic Network Process (ANP). It specifies the components 
that are vital for disaster (especially earthquake) resilience in urban areas with high levels of disaster as a result of 
earthquakes and should be considered by authorities and managers as their priorities.  

Some research has been conducted regarding disaster resilience in urban areas. 

The economic and institutional resilience of four different districts of Tehran was evaluated by Rezaei (2014). He 
assessed institutional resilience (institutional context, institutional relations, institutional efficiency) and economic 
resilience (loss rate, loss compensation capacity, ability to recover) using Analytic Hierarchy Process (AHP) and 
Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE). He found that the loss rate 
indicator (0.383) and compensation capacity indicator (0.281) had the highest importance.  

2. Methodology:

2.1 Finding the dimensions and components of DRS using the Delphi method 
The first stage was the identification of appropriate criteria for the Disaster Resilient System in urban areas. The 
Delphi method was suggested for the demonstration of the dimensions and components of DRS. The Delphi method 
originated from the studies of the RAND Corporation in the 1950s and is known as a method that is applied to a wide 
variety of fields and a tool for expert problem-solving. The Delphi study is designed to answer questions with high 



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Sarmadia/ Environmental Science and Sustainable Development 

uncertainty and based on speculation. Hence, only individuals with a high level of relevant knowledge are eligible to 
respond to these questions (Okoli and Pawlowski, 2004). 

In the first step of the Delphi method, a group of relevant experts was identified and selected. For this research, a 
group of 26 experts in the fields of disaster management, urban planning, civil and environmental engineering, and 
geography were chosen. After the identification of experts, a relevant questionnaire should be designed. Therefore, 
in the second step, we selected 16 studies in the field of disaster resilience (Table 1) and classified their selected 
factors into 6 dimensions as main elements of DRS. Then, based on the obtained dimensions and components, the 
questionnaire was designed and sent to experts. The experts were asked to respond to the questionnaire and to offer 
feedback on the position of dimensions and components and consolidation or expansion of components, or to propose 
other new elements which they thought were important for DRS.  

In the next step, the responses of experts were received and analyzed. Then, a new questionnaire was designed for 
the second round. The respondents were asked to revise their first response and answer other questions based on their 
responses to the first survey. They could change their responses or maintain their previous opinion. This process was 
repeated until more than 77% of respondents attained a component identical to that acceptable for DRS. Finally, 41 
components in 6 main dimensions were selected (Table 2). 

2.2 Determination of the intensity of the direct and indirect relationships between DRS 
components using DEMATEL 

DEMATEL is one of commonly used techniques in multiple-criteria decision-making (MCDM) which is suitable for 
the analysis of the direction and strength of the direct or indirect relationships between defined components (Chen, 
Hsu, and Tzeng, 2011). The different steps of this method are described below. 

Step 1: In this step, the direct relation average matrix should be calculated. The degree of the direct impact that each 
perspective i exerts on each perspective j is determined by respondents (Chen, Hsu, and Tzeng, 2011) using a scale 
ranging from 0 to 4 (0: no impact, 1: low impact, 2: medium impact, 3: high impact, 4: very high impact) (Chen, Lien, 
and Tzeng, 2010). The mean of the same perspective in the various direct matrices of the respondents is used to obtain 
an average matrix C (eq. 1) (Chen, Hsu, and Tzeng, 2011). 

C=[

𝑐11 𝑐1𝑗 𝑐1𝑛

𝑐𝑖1 𝑐𝑖𝑗 𝑐𝑖𝑛

𝑐𝑛1 𝑐𝑛𝑗 𝑐𝑛𝑛

]  (1) 

Step 2: The initial direct impact matrix is calculated in this step. Therefore, the average matrix C should be normalized 
to obtain the initial direct impact matrix D using equations (2) and (3). 

D=sC (2) 
s=min [ 1

𝑚𝑎𝑥 ∑ |𝑐𝑖𝑗|
𝑛
𝑗=1

,
1

𝑚𝑎𝑥 ∑ |𝑐𝑖𝑗|
𝑛
𝑖=1

]           (3) 

Step 3: The total impact matrix is calculated in this step. By obtaining the normalized direct-impact matrix D, the 
total impact matrix T can be calculated using equation (4) as follows: 

𝑇 = 𝐷 + 𝐷2 + ⋯ .+𝐷𝑘 = 𝐷(𝐼 − 𝐷)−1 (4) 

𝑇 = [𝑡𝑖𝑗]𝑛∗𝑛
i,j=1,2,…,n

(𝐼 − 𝑋)(𝐼 − 𝑋)−1 = 𝐼

Where I is the identity matrix. 

Step 4: In this step, the sum of each row and column of matrix T are obtained using equations (5) and (6). 



Sarmadia/ Environmental Science and Sustainable Development 

pg. 4 

𝑑 = [∑ 𝑡𝑖𝑗
𝑛
𝑗=1 ]

𝑛∗1
 (5)      

𝑟 = (𝑐𝑗)1∗𝑛

′
= [∑ 𝑡𝑖𝑗

𝑛
𝑖=1 ]

′
 (6) 

𝑑𝑖= the sum of the ith row of matrix T

𝑟𝑗= the sum of the jth column of matrix T

Therefore, 𝑑𝑖 illustrates the sum of direct and indirect impacts of component iIon the other components. On the other
hand, 𝑟𝑗 shows the sum of direct and indirect impacts that component j has received from the other components (Chen,
Hsu, and Tzeng, 2011). Moreover, di+rj indicates the intensity of the relationship between components, and di-rj 
indicates the intensity of impacts among components (Azizi et al., 2014).  

2.3. Applying DEMATEL data to ANP to find weighted supermatrix using Supper Decision 
software  
Finding the intensity and interrelation between the components and dimensions in a complicated system such as DRS 
is very difficult. DEMATEL is not only used to find interrelation between factors, but is also used to specify the more 
accurate weights of the pairwise comparison (Chen, Hsu, and Tzeng, 2011). Moreover, due to its nonlinear structure, 
ANP can manage interdependencies between clusters (dimensions) and nodes (components of each dimension). ANP 
is one of the MCDM methods, is an extension of AHP, and was introduced by Saaty in 1996 and 2001. A combination 
of DEMATEL and ANP is increasingly used in different subjects such as equity investment (Lee et al., 2011), 
environment watershed plans (Chen, Lien, and Tzeng, 2010), hot spring hotel performance (Chen, Hsu, and Tzeng, 
2011), brand marketing (Wangand Tzeng, 2012), and entrepreneurship policy (Tsai and Kuo, 2011).  

The first step of ANP is defining a conceptual model which determines interconnections among dimensions and 
components. The second step is a pairwise comparison between components using Super Decisions software. The 
Super Decisions software is a mathematical theory that applies ANP to find dependence and feedback of dimensions 
and components. In this step, pairwise comparison is performed through number coding (from 1 to 9) in Super 
Decisions software (Fig. 1) (Azizi et al., 2014). 

Fig 1: Pairwise comparison in Super Decisions software 

The next step is the calculation of the primary supermatrix which is obtained from weights of the pairwise comparison 
of the previous step. Equation 7 indicates the general form of the supermatrix. 

Eq. 7:   𝑤 =

D1 

𝐷2  

𝐷𝑛

𝐶11

C12

⋮
𝐶1𝑚1

𝐶21

 𝐶22

𝐶2𝑚2

⋮
𝐶𝑛1

𝐶𝑛2

⋮
𝐶𝑛𝑚𝑛

[

𝑊11 𝑊21 ⋯ 𝑊1𝑛 

 𝑊12  𝑊22 … 𝑊2𝑛

 ⋮ ⋮ ⋱ ⋮
𝑊𝑛1 𝑊𝑛2 ⋯ 𝑊𝑛𝑛]

This equation has 6 dimensions and 41 components. Therefore, it is a 41*41 supermatrix in which Wij denotes the 
connection between the ith component and the jth component. In the final step, the dimension weights are calculated 



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Sarmadia/ Environmental Science and Sustainable Development 

by weighting the primary supermatrix, which is obtained by multiplying the dimension weights matrix by the primary 
supermatrix (Azizi et al., 2014). 

3. Discussion and Results
As mentioned previously, the Delphi method was used to find the relevant dimensions and components of disaster 
resilience in urban areas. Therefore, the opinions of 26 experts, mostly with experience in disaster management and 
primary knowledge of resilience, were applied. We tried to include various perspectives in our Delphi process to 
evaluate the results. 

In the first design of the DRS, six dimensions and relevant components were introduced based on the 16 mentioned 
studies (Table 1). They were used to design a structured questionnaire. The experts were asked to comment on DRS 
and their components (dimensions and components that are thought to be appropriate for DRS). The structured 
questionnaire was sent to experts by email, fax, and rarely by mail based on their convenience. In addition to the 
questionnaire, the main base and origin of each dimension and component were provided to experts for their 
knowledge. Then, they were asked to modify the classification and the position of components if they had new ideas 
or comments. These comments were gathered and analyzed by the researchers. Identical responses were removed and 
other perspectives were consolidated to provide a new questionnaire. 

Table 1: Models and frameworks of resilience 
Model or Study Factors  

EMA (2001) 

personal attributes (health, income, age, gender, skills, network, and lifestyle choices); 
infrastructure status (coverage, accessibility, and reliability); community attributes (networks, 
amenities, and facilities); economic status and trends (growth or decline, employment levels, 
and innovation); demographic status and trends (age structure, immigration, and gender 
balance); environmental status (sustainability, diversity, and pollution); geographic attributes 
(remoteness, topography, and weather) 

Buckle (2006) shared community values, knowledge of hazards, established social infrastructure, 
positive socioeconomic trends, partnerships, resources, and skills. 

CSIRO(2007) 
metabolic flows (production, supply, and consumption chains); governance networks (institutional 
structures and organizations); social dynamics (demographics, human capital, and inequity); built 
environment (ecosystem services in urban landscapes)  

Mayunga (2007) 

Social capital (trust, norms, and networks); economic capital (income, savings, and investment); 
human capital (education, health, skills, and knowledge/information); physical capital (housing, 
public facilities, and business/industry); natural capital (resource stocks, land and water, and 
ecosystem) 

Twigg (2009) 

Governance (policy, planning priorities and political commitment, legal and regulatory systems, 
integration, institutional mechanisms, capacities and structures, allocation of responsibilities, 
partnerships, and accountability and community participation); risk assessment (hazards/risk data 
and assessment, vulnerability and impact data and assessment, and scientific and technical 
capacities and innovation); knowledge and education (public awareness, knowledge and skills, 
information management and sharing, education and training, cultures, attitudes, motivation, and 
learning and research); risk management and vulnerability reduction (environmental and natural 
resource management, health and wellbeing, sustainable livelihoods, social protection, financial 
instruments, physical protection, structural and technical measures, and planning regimes); 
disaster preparedness and response (organizational capacities and coordination, early warning 
systems, preparedness and contingency planning, emergency resources and infrastructure, 
emergency response and recovery, participation, voluntarism, and accountability). 

Cutter et al. (2008a) 

social vulnerability (race and ethnicity, age, socio-economic status, gender, employment, 
education, household structure, access to services, occupation, housing, and special needs); built 
environment and infrastructure (residential, commercial and industrial development, lifelines, 
transportation infrastructure, and monuments and icons); natural systems and exposure; hazards 
mitigation and planning (emergency response plans, building standards and codes, hazard 
mitigation plans and vulnerability assessments, comprehensive plan, COOP, interoperable 
communication, recovery plans, and etcetera)  

Cutter et al. (2008b) 

Ecological (wetland acreage and loss, erosion rate, impervious surface, biodiversity, and coastal 
defense structure); social(demographics, social network and social embeddedness, community 
values-cohesion, and faith-based organization); economic (employment, value of property, wealth 
generation, and municipal finance); institutional (participation, hazard mitigation plans, 
emergency services, zoning and building standards, emergency response plans, interoperable 
communications, and COOP); infrastructure (lifelines and critical infrastructure, transportation 
network, residential housing stock and age, and commercial and manufacturing establishments); 
community competence (local understanding and risk, counseling services, absence of 
psychopathologies, health and wellness, and quality of life). 



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pg. 6 

Model or Study Factors  

Norris et al. (2008) 

economic development (risk and vulnerability to hazards, level and diversity of economic 
resources, and equity in resource distribution); social capital (received social support, perceived 
social support, social embeddedness (informal ties), organizational linkage and cooperation, 
citizen participation, leadership and roles (formal ties), sense of community, and attachment to 
place); community competence (community action, critical reflection and problem-solving skills, 
flexibility and creativity, collective efficacy empowerment, and political partnership); information 
and communication (narratives, responsible media, skills and infrastructure, and trusted sources 
of information)   

Normandin et al. 
(2011) 

income; water; emergency planning; population; energy; insurance; telecommunication; 
environment condition; housing condition; safety standards and codes; age; education; gender; 
business; health; transportation; urban planning; community involvement; training; risk 
assessment 

Lo´pez-Marrero 
and Tschakert 

(2011) 

Suggested focusing on community-level disaster prevention supporting local authorities and civic 
organizations, building cooperative partnerships, strengthening disaster prevention knowledge and 
capabilities, and encouraging the expression of relevant thoughts or opinions on disaster 
prevention issues through the implementation process to ensure consensus building for key disaster 
management issues 

Smith et al. 
(2011) 

Suggested that community-based disaster prevention measures should provide resident 
participation and transformational approaches, including disaster management implementation 
plans, expert assistance and analysis, and capability assessment models and experience transfer, 
thus increasing public disaster prevention awareness and improving emergency and crisis response 
capabilities 

McGee (2011) 

Proposed supporting community-based disaster prevention by localizing disaster and emergency 
management responsibilities and capabilities. Recommended government support of community 
organizations and residents in implementing community-based disaster prevention, along with the 
establishment of public-private partnerships for disaster prevention and rescue work 

Ainuddin and Routary 
(2012) 

leadership and politics, structural and societal changes, physical location, age, attitudinal factors, 
health, income, gender, long-term commitment, social networks, 
government policies, short-term recovery, capable agencies, reaccumulation 
 of capital, and long-term rehabilitation 

Wilson (2013) 

Proposed the use of government channels to promote community-based disaster prevention, 
coordinate the timing of policy implementation, define the rights and responsibilities of various 
stakeholders, and control funding to ensure proper implementation of community-based disaster 
prevention measures 

Parsons et al. (2016) 

absorptive capacity was social character, economic capital, community 
capital, emergency services, planning, infrastructure, information, 
and engagement. On the other hand, adaptive capacity included themes such as governance, policy, 
leadership, social engagement, and community engagement 

Mohammed Hamidul 
Hasan et al.  (2020) 

Demographic structure, Economic capital, Social capital, Community engagement, Plan and 
Policies, Infrastructure, Existing resources, Knowledge of hazards 

In the second round, experts provided their comments on the modified questionnaire. Finally, after 4 rounds, an 
agreement was reached by experts and more than 77% of participants accepted the dimensions and components of 
Table 2 as the elements of DRS. Although some of these components such as land use can belong to different 
dimensions, they were placed in only 1 dimension based on experts’ comments. 

Table 2: Proposed dimensions and components of DRS 

Conception Dimensions Components Explanation 

Disaster 
Resilient 
System 

Economic 
dimension 

Access to services (C1) availability, diversity, frequency, and distance from services 
Economic diversity (C2) income and occupational resources in the community 

Economic growth (C3) 

Economic safety (C4) security and safety in national and international investment 
and production  

Economic stability (C5) stability in international deals and exchanges, and stability 
in national currency exchange rate  

Individual economic 
condition (C6) 

Employment, income, savings, investment, value of 
property, housing, and occupation 

Municipal finance (C7) role of governmental-based assistance during and after 
disasters  

Sustainable livelihoods (C8) occupational security, unemployment security, and 
distribution of wealth and livelihood assets 

Environmental 
dimension 

Biodiversity (C9) 
Geographic attributes (C10) remoteness, topography, and weather 

Land use (C11) 
urban open spaces such as parks and green spaces for 

settlement operations, commercial and residential use, and 
their distance to open spaces  



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pg. 7 

Conception Dimensions Components Explanation 
Natural capital (C12) resource stocks, land and water, and ecosystem 

Pollution (C13) Extension and kind of produced pollution 
Potential hazards and 

exposure (C14) 
Sustainability (C15) 

Infrastructural 
dimension 

Critical infrastructure (C16) 
national public media, nuclear facilities, internet networks, 
emergency centers, government organizations, and military 

sites 
Coverage, accessibility, and 

reliability (C17) 
coverage of, accessibility to, and reliability of critical 

infrastructures, lifelines, and public facilities 

Lifelines (C18) 
electric power facilities, water and wastewater facilities, oil 

and natural gas facilities, telecommunication, and 
transportation 

Public facilities (C19) police stations, hospitals, fire stations, schools and 
universities, and nursing homes 

Institutional and 
planning 

dimension 

Comprehensive emergency 
plan (C20) 

response plans, Continuity of Operation, recovery plans, 
preparedness and contingency planning, urban planning, 

hazard mitigation plans and vulnerability assessments, and 
infrastructure protection plan 

Early warning systems (C21) 

Governance networks (C22) connections between governmental organizations and 
agencies 

Hazard insurance (C23) acceptance and coverage of hazard insurance by the public, 
and the existence of scientific-based insurance   

Institutional mechanisms, 
capacities, and structures 

(C24) 

institutional mechanisms, capacities, and structures to 
respond to disasters and emergencies, and the role of NGOs 

and related organizations in disasters 
Legal and regulatory systems 

(C25) 
existence of emergencies and disasters laws, and law 

enforcement actions 
Responsible media (C26) role of media in disasters 

Socio-cultural 
dimension 

Attachment to place (C27) dependence on location, and neighborhoods 

Community competence 
(C28) 

local understanding and risk, counseling services, absence 
of psychopathologies, health and wellness, quality of life, 
community action, critical reflection and problem-solving 

skills, flexibility and creativity, collective efficacy and 
empowerment, and political partnership 

Demographics(C29) age structure, gender balance, populations with special 
needs, and immigration 

Education and training (C30) 
level of education, level of public information about 

disasters, level of disaster training among people, and public 
knowledge 

Household structure (C31) number of children, family network and connections, 
distance between family and their relatives, health, and age 

Sense of community (C32) 

Social network (C33) 
social cohesion, individual and public participation, race and 
ethnicity, beliefs, social embeddedness (informal ties), and 

leadership and roles (formal ties) 

Social protection (C34) social mutual assistance systems, access to social services, 
and social information channels 

Structural and 
physical 

dimension 

Hazardous facilities (C35) locations, amount, kind, and preservation activities 
Historic landmarks(C36) locations, importance, age, usage, and popularity 

Industrial and commercial 
units (C37) 

number of employees, structural conditions, kind of activity, 
and access to services 

Physical protection (C38) civil engineering activities for physical empowerment and 
strength  

Residential units (C39) structural condition, number of residents, and access to 
services  

Standards and codes (C40) 
Structural and technical 

measures (C41) 

In the second step, the experts were asked to specify the extent of the impact of each component on other components 
through the numbers 0 to 4 (0: no impact; 1: very low impact; 2: low impact; 3: high impact; 4: very high impact) 
regarding the earthquake condition and resources of urban areas. Therefore, based on the obtained data, DEMATEL 
techniques, and programming by MATLAB software, the intensity and direction of the relationship between 



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Sarmadia/ Environmental Science and Sustainable Development 

components were calculated. The outcomes were applied to introduce a conceptual model in ANP. Figure 2 illustrates 
the overall defined conceptual model of DRS in Super Decisions software. It shows how dimensions are connected 
and therefore impact each other. As mentioned previously, connections and intensity among components (connected 
to or from each component) were computed by importing DEMATEL outcomes data and defining user formulas to 
this software. 

Fig 2: The conceptual model of DRS in Super Decisions software 

In the next step, pairwise comparisons were performed between dimensions and components to specify the importance 
of dimensions and components to each other for urban areas. Finally, the results of the model were obtained as a 
weighted supermatrix, normalized components, and overall synthesized priorities for alternative dimensions in the 
following charts and tables. 



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pg. 9 

Figure 3: Urban areas dimension matrix 

The intensity between dimensions is shown in Figure 3. Data in columns indicate the impact of each dimension on 
others. It can be seen that the economic dimension of urban areas has the most impact on structural and infrastructural 
dimensions (0.21837, 0.213053), and then, on the institutional and planning dimension (0.130875). This means that 
a strong economic dimension results in powerful and resistant infrastructural and structural dimensions in urban areas. 
It also illustrates the impact of the economic dimension on other dimensions which is relatively high in comparison 
with other dimensions. The environmental dimension also has a high impact on structural and infrastructural 
dimensions (0.17263, 0.184595). Obtained data indicate that the infrastructural dimension is more influential on 
structural resilience (0.303017) and economic resilience (0.153694) in urban areas. The high impact of planning and 
socio-cultural dimension on economic dimension can also be seen in urban areas. However, in the next rank, planning 
is more influential on structural and infrastructural dimensions. It should be noted that the socio-cultural dimension 
has a reasonable impact on planning. Finally, the structural dimension also has a high impact on the infrastructural 
dimension, and then, with a significant difference, on the planning dimension. 



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pg. 10     

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 
C1 0 0 0.03539 0.04204 0.05216 0.02155 0.0616 0.01298 0 0.02226 0.01185 0.00838 0 0.01068 0.00242 0.00658 0.04131 0.00801 0.02562 0 
C2 0 0 0.0498 0.01498 0.04062 0.05661 0 0.03363 0.00833 0.00471 0 0.00205 0 0.00288 0.0022 0 0.01071 0 0 0 
C3 0 0.13965 0 0.14147 0.11516 0.14236 0 0.10607 0.04806 0.01031 0 0.00651 0 0.00661 0.01354 0.04199 0.06573 0.04864 0 0 
C4 0 0.07782 0.06237 0 0.08765 0.06106 0 0.03573 0 0 0 0.00205 0 0.00178 0.00452 0.01552 0.01797 0.01641 0 0 
C5 0 0.08074 0.07165 0.07716 0 0.06106 0 0.03573 0 0 0 0.00205 0 0.00183 0.00452 0.01552 0.01797 0.01641 0 0 
C6 0 0.01907 0.011 0.042 0.02744 0 0 0.08387 0 0 0 0.00479 0 0.00361 0.00471 0 0 0 0 0 
C7 0 0.04947 0.04465 0.03809 0.03594 0.01733 0 0 0 0.00505 0.03554 0.01678 0 0.01577 0.00652 0.07408 0 0.06422 0.12808 0 
C8 0.30801 0.02465 0.03315 0.03567 0.03243 0.03143 0.24641 0 0.01733 0.00505 0 0.00479 0.06552 0.00424 0.00894 0 0 0 0 0.30801 
C9 0 0 0 0 0 0 0 0 0 0.05921 0.02586 0.04879 0.05028 0.02221 0.03968 0 0 0 0 0 

C10 0 0 0 0 0 0 0 0 0.23356 0 0.0807 0.15884 0.31963 0.2 0 0 0 0 0 0 
C11 0.01935 0 0.00177 0 0 0 0.00312 0 0.03402 0.09315 0 0.10598 0 0.10165 0.18251 0 0 0 0 0.01935 
C12 0 0 0.01129 0 0 0.03074 0 0 0.08363 0.15195 0.0807 0 0.16085 0.06253 0.11979 0 0 0 0 0 
C13 0 0 0.00342 0 0 0.00615 0.00153 0 0.12058 0.06084 0.02303 0.02072 0 0.01503 0.02547 0 0 0 0 0 
C14 0 0 0 0 0 0 0.00849 0 0.05123 0.0382 0.18064 0.03231 0 0 0.07996 0 0 0 0 0 
C15 0.00968 0.03689 0.01255 0.03689 0.03689 0 0.01589 0.02903 0.17301 0.04405 0.05647 0.08076 0.08782 0.04598 0 0.05292 0.05292 0.05292 0.05292 0.00968 
C16 0 0 0.06087 0 0 0 0.06653 0 0 0.05869 0 0.05538 0 0.06831 0.06153 0 0.17984 0.16683 0.15415 0 
C17 0.11716 0 0.03044 0 0 0 0.01792 0 0 0.01694 0 0.01846 0 0.0184 0 0.05138 0 0.10105 0.05138 0.11716 
C18 0.05119 0 0.06087 0 0 0 0.06653 0 0 0.05448 0.1846 0.05538 0 0.06372 0.06153 0.15415 0 0 0.15415 0.05119 
C19 0.0447 0 0.06087 0 0 0 0.06207 0.21305 0 0.05448 0 0.05538 0 0.03416 0.06153 0.15415 0.17984 0.0918 0 0.0447 
C20 0 0 0.03539 0.04204 0.05216 0.02155 0.0616 0.01298 0 0.02226 0.01185 0.00838 0 0.01068 0.00242 0.00658 0.04131 0.00801 0.02562 0 
C21 0.01828 0.12473 0.02182 0.0467 0.04463 0.12473 0.02834 0.09816 0.14336 0.01434 0.00861 0.01684 0 0.0274 0.06297 0.02743 0.00844 0.0104 0.0147 0.01828 
C22 0 0 0.01387 0 0 0 0.0222 0 0 0.03598 0.02809 0.02564 0 0.02518 0 0.04331 0.0441 0.02369 0 0 
C23 0.06908 0 0.0378 0 0 0 0.02892 0 0 0 0 0 0 0.01011 0 0 0.02622 0.03804 0.05554 0.06908 
C24 0 0 0.00782 0.02245 0.01949 0.04158 0.02777 0.03272 0 0.00555 0.0132 0.00884 0 0.01466 0.01841 0 0 0 0 0 
C25 0.04352 0.04158 0.04957 0.09715 0.10218 0 0.02364 0 0 0.02618 0 0.04083 0 0.00693 0.01077 0.01581 0.0148 0.01524 0.02333 0.04352 
C26 0 0 0 0 0 0 0 0 0 0 0.04226 0 0 0.00339 0 0 0 0 0 0 
C27 0 0 0 0 0 0 0 0 0 0.0101 0 0 0 0.00448 0 0.007 0 0.00619 0 0 
C28 0 0 0 0 0 0.00424 0 0.00353 0 0.01214 0 0.00376 0.01054 0.0028 0 0 0 0 0 0 
C29 0.01739 0.08197 0.03819 0.0448 0.06064 0.01324 0.08144 0.02459 0 0.00515 0 0.01287 0.04826 0.02067 0.01945 0.01074 0.03714 0.01182 0.00952 0.01739 
C30 0.03853 0.02483 0.02307 0.01896 0.01767 0.00773 0 0.00689 0.08689 0.02273 0.04468 0.02209 0 0 0.00479 0.01702 0 0.01668 0.01855 0.03853 
C31 0.009 0 0.00841 0 0 0.01613 0.02036 0.01263 0 0 0 0 0 0 0.00801 0 0 0 0 0.009 
C32 0 0.02255 0.00415 0.01091 0.00779 0.02301 0 0.00755 0 0 0 0 0 0.00783 0.00223 0 0 0 0 0 
C33 0 0 0 0 0 0.00436 0 0.00309 0 0.0113 0.01117 0.0026 0 0.00229 0 0 0 0 0 0 
C34 0.03077 0 0.0171 0 0 0.03288 0 0.02359 0 0 0 0.00891 0 0.01503 0.01553 0.00643 0 0.00577 0.00619 0.03077 
C35 0.00611 0 0.01088 0.05468 0.04326 0.02777 0 0.01992 0 0.00454 0 0.00564 0.01842 0.00723 0.00585 0.00294 0 0.00288 0.00287 0.00611 
C36 0 0 0.01066 0 0 0 0 0 0 0.01278 0.02849 0.06601 0 0.01982 0.00671 0.08132 0.03528 0.03823 0.04759 0 
C37 0 0.13803 0 0 0 0 0 0 0 0.01133 0.048 0.01026 0 0.00582 0.00977 0 0 0 0 0 
C38 0 0.13803 0.10797 0.20704 0.18403 0.01704 0.01484 0.11017 0 0.01278 0 0.03346 0 0.01008 0.02942 0.18618 0.08493 0.07913 0.17988 0 
C39 0 0 0.02347 0 0 0.03635 0.07955 0.04428 0 0.04467 0.01449 0.01428 0 0.05176 0.05917 0 0 0 0 0 
C40 0.02848 0 0.04905 0 0 0.15776 0.01484 0.01851 0 0.01278 0 0.03346 0.23868 0.01439 0.0202 0.03552 0.02648 0.02708 0.07555 0.02848 
C41 0.04521 0 0 0 0 0 0.0343 0 0 0.03363 0.06514 0 0 0.02915 0 0 0 0 0 0.04521 

Table 3: Urban areas weighted supermatrix 



Sarmadia/ Environmental Science and Sustainable Development 

pg. 11     

C21 C22 C23 C24 C25 C26 C27 C28 C29 C30 C31 C32 C33 C34 C35 C36 C37 C38 C39 C40 C41 

C1 0 0.02679 0.06635 0 0 0 0 0 0 0.02076 0 0 0 0 0 0.09862 0 0 0.01233 0 0.01409 

C2 0 0 0 0 0 0 0 0 0 0.007 0 0 0 0 0 0 0.01263 0.00274 0 0 0 

C3 0.10049 0.07364 0 0 0 0 0 0 0.12755 0.05198 0.02235 0 0 0 0 0 0.03773 0.02475 0 0 0 

C4 0.01819 0.01721 0 0 0 0 0 0 0 0.01285 0 0 0 0 0 0 0 0.00727 0 0 0 

C5 0.01819 0.01721 0 0 0 0 0 0 0 0.01285 0 0 0 0 0 0 0 0.00727 0 0 0 

C6 0 0 0.01639 0.09332 0 0.0633 0 0 0.01857 0.08023 0.16468 0.18577 0 0.24769 0.27495 0 0 0.00495 0 0.04437 0 

C7 0.07604 0.11839 0.14028 0.2091 0.34002 0.18993 0.3548 0 0.06757 0 0 0 0 0 0 0 0.08455 0.03623 0.08629 0.10158 0.08453 

C8 0.04033 0 0.03021 0.03123 0 0 0 0 0.03401 0.04438 0.06067 0.06192 0 0 0 0 0 0.0154 0 0.01938 

C9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

C10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

C11 0.00881 0 0.00776 0 0 0 0 0 0 0.0018 0 0 0 0 0 0 0.08168 0 0 0 0.01194 

C12 0 0 0 0 0 0 0 0 0 0.01489 0.01011 0.03103 0 0 0 0.0361 0 0 0.03687 0.08342 0 

C13 0.00219 0 0 0 0 0 0 0 0 0.00356 0.00321 0.00776 0 0 0 0.00626 0 0 0.00511 0.01668 0 

C14 0.0159 0.02661 0 0 0 0 0 0 0.00646 0.00958 0 0 0 0.0097 0 0 0 0 0 0 0 

C15 0.00414 0.00443 0.02328 0 0.04168 0.02661 0 0 0.03232 0.00619 0.02547 0 0 0.02909 0.04305 0.01735 0 0.05971 0.01773 0 0.04777 

C16 0.03318 0.05036 0.02323 0 0 0.03871 0 0 0 0.02375 0 0 0 0 0 0.05095 0 0.08966 0.04483 0 0.0807 

C17 0.01659 0.01029 0.04646 0 0 0 0 0 0 0 0 0 0 0 0 0.13874 0 0 0.13449 0 0.0269 

C18 0.03318 0.02774 0.02323 0 0 0.03871 0 0 0 0.02375 0 0 0 0 0 0.05095 0 0.08966 0.04483 0.22548 0.0807 

C19 0.03318 0.02774 0.02323 0 0 0.03871 0 0 0 0.02375 0 0 0 0 0 0.02835 0 0.08966 0.04483 0.22548 0.0807 

C20 0 0.0983 0.255 0.48306 0.13768 0.08908 0.06021 0.13979 0.03519 0.03088 0.09974 0.09974 0.18638 0.02063 0.14762 0.01357 0.18005 0.09871 0.03767 0.22066 0.03021 

C21 0.06593 0 0 0 0.04609 0 0.31561 0 0 0 0 0 0 0.00949 0 0.07625 0 0 0.00792 0 0 

C22 0.12696 0 0 0 0.30851 0.17036 0.13786 0 0 0 0 0 0 0.03248 0 0 0 0 0 0 0 

C23 0.01747 0.02508 0.02765 0 0 0.05243 0 0.0466 0.00719 0 0.03325 0.03325 0 0.00627 0 0.03507 0 0 0.06569 0 0.01538 

C24 0.08051 0.04921 0.08397 0 0 0.03626 0 0 0.02664 0.09264 0 0 0 0 0 0.00673 0 0.0329 0.02034 0 0.00833 

C25 0.04559 0 0 0 0 0 0 0 0.05083 0 0 0 0 0 0 0 0 0 0 0 0.0777 

C26 0.03016 0.19403 0 0 0 0.0185 0 0 0.01315 0 0 0 0 0.06412 0 0 0 0 0 0 0 

C27 0 0 0 0 0 0 0 0 0 0.04661 0.02033 0.10733 0.29054 0.14267 0.04284 0 0.01378 0 0 0.00788 0 

C28 0.06448 0.02519 0.02396 0 0.09452 0.09386 0.03288 0.0497 0 0.01809 0.07498 0.03254 0.05434 0.02233 0.05273 0.03004 0 0 0 0 0.03755 

C29 0 0 0 0 0 0 0 0.10411 0.04516 0 0.04106 0.06005 0.12311 0.11447 0.10233 0 0 0 0 0.03444 0 

C30 0.01188 0 0 0 0 0 0 0 0.22293 0.04763 0 0 0 0.017 0 0 0 0 0 0 0 

C31 0 0 0 0 0 0 0 0 0 0.14114 0.16298 0 0 0.06828 0.08704 0 0 0 0 0.01636 0 

C32 0 0 0 0 0 0 0 0.18442 0.02227 0.04526 0.02033 0.12967 0 0.08599 0.03041 0 0.00602 0 0 0.00427 0 

C33 0 0.05767 0.06039 0 0.03151 0 0.09864 0.25437 0.12749 0.10304 0.10896 0.12716 0.16151 0 0.21902 0 0.03156 0.03004 0 0 0 

C34 0.0175 0.011 0.00951 0 0 0 0 0.08209 0.06355 0.04534 0.05277 0.02465 0.04518 0.03065 0 0.00751 0 0.00751 0.03755 0 0 

C35 0.03782 0.03991 0 0 0 0.01869 0 0 0 0.01104 0 0 0 0 0 0 0 0 0.07812 0 0.05828 

C36 0.00547 0.00565 0 0 0 0 0 0 0 0.00667 0 0 0 0 0 0.03641 0 0 0.01584 0 0 

C37 0.02136 0.03544 0.0754 0 0 0.00648 0 0 0 0.02467 0 0 0 0 0 0.26827 0 0 0.05284 0 0.03714 

C38 0.00899 0.00768 0 0 0 0.02358 0 0 0.02478 0 0.04957 0 0 0.04957 0 0 0 0 0 0 0.14222 

C39 0.03275 0.02709 0.01902 0.18329 0 0.00979 0 0.13894 0 0.04968 0 0.09913 0.13894 0 0 0.09883 0 0.40352 0.03001 0 0.02366 

C40 0.01377 0.01445 0 0 0 0.05384 0 0 0 0 0 0 0 0 0 0 0.4416 0 0 0 0 

C41 0.01895 0.00887 0.03588 0 0 0.02673 0 0 0.07435 0 0.04957 0 0 0.04957 0 0 0.1104 0 0.22671 0 0.14222 



Sarmadia/ Environmental Science and Sustainable Development 

pg. 12     

Table 3 illustrates the weighted supermatrix which is the result of pairwise comparison of components. This table 
also represents the connection or disconnection of components with each other and the impacts of each component 
on the other components. The impact of each component on the other components is shown in each column. It can be 
seen that potential hazards and exposure, natural capital, and geographic attributes of urban areas have the highest 
impacts on the other components. The comprehensive emergency plan (CEP) is also one of the vital components of 
urban areas, which is under the influence of most components.  

Charts 1 to 6 illustrate overall synthesized priorities of urban areas for the dimensions which have been represented 
in 3 forms; ideal, normal, and raw which are based on three different methods by idealzing or normalizing the values. 
The first normal priority in the economic dimension is municipal finance. Sustainable livelihood and economic growth 
are, respectively, in the second and third rank (Chart 1). Sustainability, natural capital, and land use are the priorities 
of the environmental dimension (Chart 2). As can be observed in Chart 3, the infrastructural priorities are critical 
infrastructure, lifelines, and public facilities which are close to each other. The CEP is the priority of the planning 
and institutional dimension. Governance networks and institutional mechanisms, capacities, and structures are the 

Chart 1: Overall synthesized priorities for the 
economic dimension 

Chart 2: Overall synthesized priorities for the 
environmental dimension 

Chart 3: Overall synthesized priorities for the 
infrastructural dimension 

Chart 4: Overall synthesized priorities for the 
institutional and planning dimension 

Chart 5: Overall synthesized priorities for the socio-
cultural dimension 

Chart 6: Overall synthesized priorities for the 
structural and physical dimension 



pg. 13     

Sarmadia/ Environmental Science and Sustainable Development 

next priorities (Chart 4). Community competence and social network are also close in socio-cultural dimension and 
demographics are the subsequent priority (Chart 5). Chart 6 illustrates that industrial and commercial units and 
residential units are in the highest normal priority. 

Table 4: Prioritized components of resilience in urban areas 

Dimensions Components Score* Dimensions Components Score* 

Economic 
(20.2%) 

Access to services (C1) 1.7209 

Institutional 
and 

planning 
(17.7%) 

Comprehensive emergency plan 
(C20) 6.9827 

Economic diversity (C2) 0.5676 Early warning systems (C21) 1.8935 
Economic growth (C3) 3.3122 Governance networks (C22) 3.1218 
Economic safety (C4) 1.0929 Hazard insurance (C23) 1.5451 

Economic stability (C5) 1.1122 Institutional mechanisms, 
capacities, and structures (C24) 2.709 

Individual economic 
condition (C6) 2.5234 Legal and regulatory systems 

(C25) 0.5647 

Municipal finance (C7) 6.4238 Responsible media (C26) 0.877 
Sustainable livelihoods (C8) 3.4959 

Socio-
cultural 
(12.3%) 

Attachment to place (C27) 0.9722 

Environment 
(7.8%) 

Biodiversity (C9) 0.301 Community competence (C28) 2.7394 
Geographic attributes (C10) 0.7003 Demographics(C29) 1.7455 

Land use (C11) 1.056 Education and training (C30) 1.1045 
Natural capital (C12) 1.5296 Household structure (C31) 0.9598 

Pollution (C13) 0.4467 Sense of community (C32) 0.8087 
Potential hazards (C14) 0.7953 Social network (C33) 2.6542 

Sustainability (C15) 2.9616 Social protection (C34) 1.3013 

Infrastructure 
(20.1%) 

Critical infrastructure (C16) 4.953 

Structural 
(21.9%) 

Hazardous facilities (C35) 2.2482 
Coverage, accessibility, and 

reliability (C17) 2.7038 Historic landmarks(C36) 0.4567 

Lifelines (C18) 5.968 Industrial and commercial units 
(C37) 5.8221 

Public facilities (C19) 6.4885 Physical protection (C38) 2.4908 
* Residential units (C39) 5.7727 

Standards and codes (C40) 0.7692 
Structural and technical 

measures (C41) 4.3084 

The next analysis of the DRS was the analysis of normalized components by dimension which has been illustrated in 
table 4. This table represents the components that should be noticed for resilience in urban areas and illustrates the 
prioritized components of urban areas. It has been shown that the CEP is the priority of urban areas and the most 
effective component of the system. This means that experts believe there are some deficiencies in the CEP for urban 
areas. This also shows the role of emergency plans in resilience in urban areas. With minor differences, sustainability 
stands in second place. The next 8 important components for urban areas are, respectively, public facilities, municipal 
finance, lifelines, industrial and commercial units, residential units, critical infrastructure, community competence, 
and social network. It is evident that the majority of priorities are related to structural and infrastructural resilience in 
urban areas. Among the final 10 priorities, the outstanding role of social components can be observed in urban areas 
resilience.  

4. Conclusion:
Since the use of the resilience concept in disaster risk reduction (Alexander, 2013), various models and frameworks 
have been proposed by scholars and scientists. Moreover, resilience has been analyzed in different fields, from 
environment to engineering and disasters. Although there is a variety of frameworks and models in disaster resilience, 
there is no consensus among researchers on components and standard metrics of resilience and the variables that 
should be used to measure resilience have not been determined (Ainuddin and Routray, 2012, Cutter et al., 2008b, 
Twigg, 2009). In this article, the physical and nonphysical dimensions of resilience were examined for urban areas 
Disaster Resilient System and the relationship between selected components and dimensions was specified using 
DEMATEL and ANP. 



pg. 14     

Sarmadia/ Environmental Science and Sustainable Development 

Whenever an emergency occurs, communities in urban areas are confronted with disturbances and interruptions; 
therefore, destruction and entropy are created based on community vulnerability. The community system of urban 
areas is resilient and has the capacity to cope with disasters if DRS is performed and implemented accurately. The 
selected elements, which consisted of 6 dimensions and 41 components, were determined using the Delphi method. 
ANP and DEMATEL were used to find the interactions between components in addition to their weights. Although 
ANP determines interactions between components, it cannot specify the weights of the components (Azizi et al., 
2014). Therefore, the application of DEMATEL was suggested in combination with ANP to increase accuracy. As a 
result, the direction and the intensity of the relationship between selected dimensions and components were calculated 
using DEMATEL. Finally, the weight and importance of the components were obtained through ANP. It should be 
mentioned that due to the time-consuming process of responding to the questionnaire, it was very difficult to convince 
participants to take their time to complete it.  

One of the key findings of this research is dimensional interrelationships. According to the findings, structural and 
infrastructural dimensions have the most impact on each other. Furthermore, the institutional and planning dimension 
and socio-cultural dimension have the most impact on the economic dimension. Furthermore, the economic dimension 
also has a noticeable impact on structural and infrastructural dimensions. 

According to experts’ comments and the analysis conducted using ANP and DEMATEL, the first prioritized 
component for contingency earthquake of urban areas is a CEP. This is mostly due to the lack of existence of a CEP 
for urban areas which should contain response plans, continuity of operation plans, recovery plans, preparedness and 
contingency plans, urban plans, disaster risk reduction plans, and infrastructure protection plans. These are pieces of 
a puzzle that should be defined with respect to each other. It is suggested that the TDMMO, as a responsible 
organization in disaster management in urban areas, provide a CEP for urban areas as one of the vital criteria in 
responding to this devastating disaster in urban areas. For the second priority of urban areas resilience, the 
sustainability indexes of urban areas should be recognized and defined. Moreover, in the first 10 prioritized 
components, the important role of public facilities resilience, lifelines resilience, industrial and commercial resilience, 
residential resilience, and critical infrastructural resilience was noticeable. This finding indicates that, besides other 
responsible organizations and agencies, TDMMO should implement projects to improve the resilience of the 
mentioned components.  

Finally, experts believe that community competence and social networks are in a reasonable condition in urban areas; 
however, they have not been well-implemented in the CEP for urban areas. Therefore, it is suggested that authorities 
and managers consider these components as nonphysical components of resilience in their programs and decisions. 

Acknowledgments 
The abstract of this paper was presented in Resilient and Responsible Architecture and Urbanism (RRAU) – 5th 
Edition which was held on the 20th of April 2023. 

Funding declaration: 

This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit 
sectors/individuals. 

Ethics approval:  

Not applicable. 

Conflict of interest:  

The authors declare that there is no competing interest. 

References: 
Ainuddin, S. and Routray, J. (2012). Community resilience framework for an earthquake prone area in Baluchistan. International Journal of 

Disaster Risk Reduction, 2, pp. 25-36. 

Alexander, D. (2013). Resilience and disaster risk reduction: an etymological journey. Nat. Hazards Earth Syst. Sci., 13(11), pp. 2707-2716. 



Sarmadia/ Environmental Science and Sustainable Development 

pg. 15     

Alliance, R. (2007). Urban resilience research prospectus: a resilience alliance initiative for transitioning urban systems towards sustainable futures. 
Australia/USA/Sweden: CSIRO/Arizona State University/Stockholm University. 

Azizi, A., Malekmohammadi, B., Jafari, H., Nasiri, H. and AminiParsa, V. (2014). Land suitability assessment for wind power plant site selection 
using ANP-DEMATEL in a GIS environment: case study of Ardabil province, Iran. Environmental Monitoring and Assessment, 186(10), 
pp.6695-6709. 

Buckle, P., Marsh, G., and Smale, S. (2001). Assessing resilience & vulnerability: Principles, strategies &actions.Emergency Management 
Australia (EMA). 

Buckle, P. 2006. “Assessing social resilience.” In Disaster resilience: An integrated approach, edited by D. Paton and D. Johnston, 88–103. 
Springfield, IL: Charles C Thomas Publisher. 

Chen, F., Hsu, T. and Tzeng, G. (2011). A balanced scorecard approach to establish a performance evaluation and relationship model for hot spring 
hotels based on a hybrid MCDM model combining DEMATEL and ANP. International Journal of Hospitality Management, 30(4), pp.908-
932. 

Chen, Y., Lien, H. and Tzeng, G. (2010). Measures and evaluation for environment watershed plans using a novel hybrid MCDM model. Expert 
Systems with Applications, 37(2), pp.926-938. 

Cutter, S., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E. and Webb, J. (2008a).  Community and Regional Resilience: Perspectives from 
Hazards, Disasters, and Emergency Management. CARRI Research Report,1. 

Cutter, S., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E. and Webb, J. (2008b). A place-based model for understanding community resilience 
to natural disasters. Global Environmental Change, 18, pp. 598-606. 

Hasan, M. H and Kadir, S. B. 2020, “Social Assessment of Community Resilience to Earthquake in Old Dhaka” Nat. Hazards Rev., 2020, 21(3): 
05020004 

Japan International Cooperation Agency (JICA). (2000). The study on seismic microzoning of the greater Terhan area in the Islamic Republic of 
Iran. Main report. 

Lee, W., Huang, A., Chang, Y. and Cheng, C. (2011). Analysis of decision making factors for equity investment by DEMATEL and Analytic 
Network Process. Expert Systems with Applications, 38(7), pp.8375-8383. 

Lo´pez-Marrero T, Tschakert P (2011) From theory to practice: building more resilient communities in floodprone 

areas. Environ Urban 23(1):229–249 

Mayunga, J. S. (2007). Understanding and applying the concept of community disaster resilience: a capital-based approach. Summer academy for 
social vulnerability and resilience building, 1, p.16. 

McGee TK (2011) Public engagement in neighbourhood level wildfire mitigation and preparedness: case studies from Canada, the  US and 
Australia. Environ Manage 92(10):2524–2532 

Mitchell, T., and Harris, K. (2012). Resilience: A Risk Management Approach.ODI Background Note. National Academy. (2012). Disaster 
Resilience: a National Imperative, Committee on Increasing National Resilience to Hazards and Disasters, Washington D.C. 

Normandin, J. M., Therrien, M. C., and Tanguay, G. A. (2009, June). City strength in times of turbulence: strategic resilience indicators. In Proc. 
of the Joint Conference on City Futures, Madrid, pp. 4-6. 

Norris, F. H., Stevens, S. P., Pfefferbaum, B., Wyche, K. F., and Pfefferbaum, R. L. (2008). Community resilience as a metaphor, theory, set of 
capacities, and strategy for disaster readiness. American Journal of community psychology, 41(1-2), pp.127-150. 

Obrist, B., Pfeiffer, C., and Henley, R. (2010). Multi‐layered social resilience a new approach in mitigation research. Progress in Development 
Studies, 10(4), pp.283-293. 

Okoli, C., and Pawlowski, S. D. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information & 
management, 42(1), pp.15-29. 

Parsons, M., S. Glavac, P. Hastings, G. Marshall, J. McGregor, J. McNeill, P. Morley, I. Reeve, and R. Stayner. 2016. “Top-down assessment of 
disaster resilience: A conceptual framework using coping and adaptive capacities.” Int. J. Disaster Risk Reduct. 19 (Oct): 1–11. https://doi.org 
/10.1016/j.ijdrr.2016.07.005. 

Rezaei, M. R. (2013). Evaluating the economic and institutional resilience of urban communities to natural disasters using PROMOTHE technique, 
case study: Tehran districts.Disaster Management Scientific Journal, No. 3.27-38. 

Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922). Pittsburgh: RWS publications. 

Saaty, T. L. (2001). The analytic network process: decision making with dependence and feedback; the organization and prioritization of 
complexity. Rws publications. 

Smith TF, Daffara P, O’Toole K, Matthews J, Thomsen DC, Inayatullah S, Graymore M (2011) A method for building community resilience to 
climate change in emerging coastal cities. Futures 43(7):673–679 

Tsai, W. H., and Kuo, H. C. (2011). Entrepreneurship policy evaluation and decision analysis for SMEs. Expert Systems with Applications, 38(7), 
pp.8343-8351. 

Twigg, J. (2009). Characteristics of a disaster-resilient community: a guidance note (version 2). Wang, Y. L., and Tzeng, G. H. (2012). Brand 
marketing for creating brand value based on a MCDM model combining DEMATEL with ANP and VIKOR methods. Expert Systems with 
Applications, 39(5), pp. 5600-5615. Wildavsky, A. (1988). Search for safety. New Brunswick: Transaction Books. 

Wilson GA (2013) Community resilience, policy corridors and the policy challenge. Land Use Policy 

31:298–310