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Volume 38, Issue 3 (2023)
GeoRes 2023, 38(3): 355-364 |
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Received: 2023/06/12 | Accepted: 2023/09/16 | Published: 2023/09/29
Received: 2023/06/12 | Accepted: 2023/09/16 | Published: 2023/09/29
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Maleki K, PourMohammadi M, Yousefi Shahir H, Masoudi Asl B. Factors Affecting the Earthquakes and Evaluation of the Vulnerability of Municipal Areas From the Point of View of Passive Defense in Kermanshah Metropolis. GeoRes 2023; 38 (3) :355-364
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1- Department of Geography and Urban Planning, Faculty of Planning and Environmental Sciences, Tabriz University, Tabriz, Iran
2- Department of Geography and Urban Planning, Marand Branch, Islamic Azad University, Marand, Iran
3- Deputy of Architecture and Urbanism of Kermanshah Municipality, Kermanshah, Iran
2- Department of Geography and Urban Planning, Marand Branch, Islamic Azad University, Marand, Iran
3- Deputy of Architecture and Urbanism of Kermanshah Municipality, Kermanshah, Iran
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Introduction
Urban development and land cover change have been among the most significant human interventions in nature during the twentieth and twenty-first centuries. Currently, more than 70% of the world’s population resides in cities [Maleki et al., 2022]. It is predicted that a substantial portion of population growth will occur in developing countries, where approximately 3.5 billion people already live. This further highlights the importance of addressing and mitigating crises in urban areas [Ziari & Ehsanifard, 2022]. An urban crisis is defined as a sudden event accompanied by extensive human and material losses, requiring immediate actions at the city level to control and contain the crisis and ensure the safety of urban areas under seismic risk [Shams et al., 2011; Alexander, 2002].
Urban vulnerability to natural hazards such as earthquakes can be considered an outcome of human behavior, underscoring the role of planning systems in reducing the destructive impacts of such events [Rashed et al., 2017; Maleki, 2023]. According to forecasts, by 2030, nearly 6 billion out of the projected 8.1 billion global population will be living in cities, with about two-thirds of them residing in metropolitan areas [Alshehabi, 2014]. In Iran, on average, one earthquake with a magnitude of 6 occurs annually, and one with a magnitude of 7 on the Richter scale occurs every decade. The 7.3-magnitude earthquake of November 12, 2017, in Ezgeleh, Kermanshah, is one such example [Maleki et al., 2022]. Therefore, given the importance of crisis management and the necessity of reducing its consequences, it is essential that urban and regional planning integrate civil defense principles as a fundamental element of urban defense planning. Passive defense encompasses measures adopted prior to the occurrence of hazards, facilitating crisis management [Hosseinzadeh Dalir et al., 2012].
Today, coping with hazards is one of the major challenges for most countries [Cutter et al., 2016], as these hazards not only cause loss of life and emotional suffering but also inflict significant damage on local economies [Bazrafshan et al., 2018]. The objective of passive defense projects is to reduce the vulnerability of vital, sensitive, and critical urban facilities and infrastructures under crisis conditions [Soltani et al., 2017]. Earthquake vulnerability can be minimized by adhering to the principles of passive defense. In essence, the core components of passive defense consist of: (1) crisis recognition and identification of potential threats, (2) hazard analysis and assessment of probable risks, (3) foresight and formulation of strategies for hazard mitigation and control, and (4) providing related considerations, provided they align with passive defense principles, with the ultimate goal of deterrence, preservation, and reinforcement of sustainable security within the geographic environment while reducing vulnerability [Pourmohammadi & Maleki, 2021; Pourmohammadi & Maleki, 2016].
Over time, seismic vulnerability in urban areas has increased due to the growing complexity of urban environments [Alavi et al., 2016]. Vulnerability is generally defined as a concept that helps to understand the living conditions under which a hazard can escalate into a disaster [Tapsell et al., 2010]. Both structural and environmental stress factors contribute to urban vulnerability in metropolitan areas. Structural characteristics include city size, density, spatial form, socioeconomic development, and infrastructure, all of which serve as key indicators for evaluating the physical vulnerability of urban areas. Environmental stress factors, on the other hand, are disturbances arising from unexpected events, such as natural disasters, accidents, public health emergencies, and health-related incidents [Chanliang et al., 2011].
Determinants of vulnerability are highly diverse (natural, physical, economic, and social factors) and interact not in isolation but as components of an integrated system [Paton & Johnson, 2001]. If vulnerability is considered as a mathematical function, it represents the predicted degree of damage for each exposed element under a hazard of a certain intensity [Zangiabadei et al., 2008]. For accurate assessment, composite layers and required maps can be generated using GIS [Church & Murray, 2008]. Hazard zoning is a crucial aspect of pre-crisis management processes [Karami & Amirian, 2018]. By evaluating earthquake hazard potential, the required precautions can be implemented to prevent large-scale tragedies and the loss of numerous lives [Khatsü, 2011].
The metropolis of Kermanshah has historically served as a residential, communicational, and demographic hub in western Iran. Considering the city’s demographic capacity and strategic role, analyzing earthquake vulnerability is indispensable. The objective of this study is to assess the vulnerability of municipal districts in the metropolis of Kermanshah from a passive defense perspective and to examine the factors influencing earthquake risks.
Methodology
This study is descriptive in nature and was conducted in 2021 using survey methods and data analysis in the metropolis of Kermanshah. As the most important demographic, political, and physical center of Kermanshah Province, the city covers an urban area of over 11,000 hectares and includes eight municipal districts. According to the latest census in 2016, the population of the city was 946,651. However, in 2017, following the annexation of several villages, the population exceeded one million, and in 2018 the city was officially recognized as a metropolis by the Ministry of Interior [Akbari et al., 2021]. Kermanshah is affected by several active and semi-active faults both within and around the city. The presence of three major fault lines passing through or adjacent to the city has made this urban area seismically vulnerable. Historical earthquakes indicate that factors such as magnitude, shallow depth, timing of occurrence, long duration, and proximity of epicenters to the city have led to severe damages (Maleki, 2018; Maleki, 2020).
The statistical population of the study consisted of experts and scholars specializing in earthquake and passive defense. Based on Cochran’s formula, a sample size of 30 individuals was selected across the eight municipal districts. To assess the reliability of the questionnaire items, Cronbach’s alpha was calculated at 0.925, which, being higher than 0.70, confirmed the instrument’s reliability. Natural, physical, social, and economic components and their indicators were designed in accordance with the objectives and principles of urban passive defense and aligned with prior studies, including academic articles and research projects. Subsequently, a table of influential components contributing to earthquake vulnerability in Kermanshah metropolis was developed based on expert input. Surveys were conducted accordingly, and the identified components were first qualitatively and then quantitatively scored across municipal districts.
The vulnerability of Kermanshah’s eight municipal districts was then assessed across the four domains using the Fuzzy TOPSIS model. Following analysis of the survey results, the output was spatially represented through maps generated with ArcGIS 10.2 software. Finally, the districts were compared in terms of physical development and other relevant indicators.
Analytic Hierarchy Process (AHP) and Eigenvector Method
In the first step, the arithmetic or geometric mean of each row was calculated, and then normalized by column to derive the weight vector. In the second step, the pairwise comparison matrices completed by the participants were analyzed. An approximate method for calculating the eigenvector W involved successive powering of matrix D.
In the third step, multiple judgments were aggregated into a comparison table, with the geometric mean being the preferred method.
The consistency ratio was verified to be below 0.1, confirming acceptable consistency. Ultimately, the sum of each row was divided by the total sum of the matrix to obtain criterion weights.
The final weights confirmed that all components provided a comprehensive representation of the research problem.
Fuzzy TOPSIS Model
To prioritize Kermanshah’s municipal districts in terms of earthquake vulnerability, the Fuzzy TOPSIS model was employed. As a well-established multi-criteria decision-making (MCDM) method, Fuzzy TOPSIS ranks alternatives based on their distance from the positive and negative ideal solutions.
In the first stage, linguistic variables collected via the questionnaire for all indicators and districts were tabulated. Qualitative variables were then converted into quantitative measures for analysis. Subsequently, distances from the negative and positive ideals were computed.
Finally, the closeness coefficient was calculated. Alternatives with higher coefficients (closer to 1) were considered closer to the ideal solution.
Findings
To analyze earthquake vulnerability in Kermanshah, the relevant components and indicators influencing this process were first identified, followed by an assessment of the city’s vulnerability through prioritization of these indicators. Based on prior studies on urban development from the perspective of earthquake risk, natural, social, economic, and physical components are consistently considered. One of the main analytical approaches for evaluating the structure of such components is the eigenvector technique, which estimates the degree of vulnerability. Accordingly, the findings of the descriptive analysis were first presented, followed by inferential results.
It is evident that lower levels of earthquake vulnerability correspond to reduced human and financial losses, and vice versa. Therefore, in order to achieve effective planning and risk assessment, the selection and refinement of relevant components and indicators must be conducted with precision. Within the structural and spatial context of Kermanshah, several factors including proximity to or direct location on fault lines, large urban area, lack of sufficient infrastructure, and others play a significant role in increasing vulnerability. In this study, natural, physical, social, and economic components and their corresponding indicators were identified in alignment with the principles of urban passive defense and in accordance with similar studies. These indicators served as input data for analyzing the vulnerability of Kermanshah’s eight municipal districts.
The influence of each component was then measured using the Shannon entropy model, while expert opinions from specialists in planning and earthquake studies were collected to prepare a qualitative analysis of the components. Finally, experts’ evaluations were quantified into a ranking of the city’s districts in terms of earthquake vulnerability. In this ranking system, the values 9, 7, 5, and 3 corresponded to “very suitable,” “suitable,” “unsuitable,” and “very unsuitable,” respectively. After scoring by the experts, the vulnerability status of Kermanshah’s districts was analyzed using the fuzzy TOPSIS model. In the next step, the output maps were analyzed to compare districts in terms of physical development and other factors, which yielded practical insights such as identifying optimal directions for future development and reducing the critical impacts of earthquakes by recognizing highly vulnerable areas.
Among the components, the physical dimension held less significance compared to others, while the social and economic dimensions also showed lower weight, whereas the natural component was found to have the highest influence. In the eigenvector analysis, a micro epsilon vector value was applied, the final weights were calculated, and the consistency ratio was found to be below 0.1, confirming the acceptability of the results (since a higher ratio would have required revision). The eigenvector analysis demonstrated that the natural component ranked first in importance, followed by the physical component in second place, while the social and economic components ranked lower.
The selected components were then evaluated across Kermanshah’s eight municipal districts to assess their suitability. The results indicated that most districts were “suitable” in terms of natural and economic components. For the social component, districts 1 and 8 were evaluated as “very suitable,” whereas districts 2, 3, 6, and 7 were found “unsuitable,” requiring attention and improvement. Regarding the physical component, districts 1, 4, and 6 were deemed “very suitable,” while districts 2, 3, 5, 7, and 8 were considered “unsuitable,” calling for corrective measures. These differences highlight the need for context-specific decisions to improve earthquake vulnerability management.
The fuzzy evaluation results for each component and district were calculated. Subsequently, normalization was carried out using the Euclidean norm method. The square root of the sum of squared lower bounds for the eight districts was calculated for each index, and the same was done for the middle and upper bounds. These values were then divided by the respective bounds of the Euclidean norm, resulting in a normalized matrix. The indicator weights, obtained through eigenvector or Shannon entropy methods, were expressed in triangular form. The distances of each option from the positive and negative ideal solutions were then calculated using the corresponding formulas.
The relative closeness of each option to the ideal solution determined their ranking. The results revealed the following ranking: District 8 in first place, District 4 in second, District 5 in third, District 1 in fourth, Districts 2 and 3 in fifth, District 6 in sixth, and District 7 in seventh place.
According to the results of the fuzzy TOPSIS model, districts 8, 4, 5, and 1 had the lowest levels of vulnerability, while the other districts (7, 6, 3, and 2) demonstrated relatively unfavorable conditions. However, in general, the open and green spaces of districts 5 and 2, and to some extent district 6, were found to be in relatively poor condition.
Discussion
The findings indicate that what often transforms an earthquake hazard into a catastrophe in urban areas is the inadequacy of urban planning and management. Therefore, by applying passive defense principles consistent with urban planning and civil defense considerations such as land-use planning, decentralization, and restricting development in proximity to high-risk or hazardous facilities the vulnerability of cities to earthquakes can be significantly reduced.
Moreover, assessing earthquake hazard potential lowers the level of uncertainty and necessary precautions, thereby preventing large-scale tragedies and loss of life [Khatsü, 2011]. Given the demographic significance of the Kermanshah metropolis, analyzing its earthquake vulnerability is inevitable. The objective of this study was to evaluate the vulnerability of Kermanshah’s municipal districts from the perspective of passive defense and to examine influencing factors. The results suggest that in formulating preparedness and crisis management strategies, initial attention must be given to variables such as fault line proximity and land slope percentage. Next, factors such as proximity to major streets, fire stations, healthcare centers, and land use, as well as social aspects (e.g., household and population density), should be considered. Finally, economic components such as household dependency ratios, active/inactive population densities, and unemployment rates reveal their importance in maintaining socioeconomic resilience following an earthquake, thereby enhancing the preparedness of Kermanshah to cope more effectively with seismic hazards [Ebrahimi et al., 2016].
The ranking of Kermanshah’s municipal districts in terms of earthquake vulnerability using the fuzzy TOPSIS model showed that higher-ranked districts in terms of risk require urgent attention and immediate crisis management measures. Lower-ranked districts, while less exposed, still require planning to strengthen resilience against seismic hazards. Such analyses provide a foundation for developing strategies and managerial decisions in preparedness and response planning.
The results of this study are aligned with those of Isalu et al. (2015), who have assessed the physical vulnerability of urban districts in Tehran’s District 1 using the IHWP hierarchical analysis method in ArcGIS. Similarly, the work of Mohammadpour et al. (2016) corresponds with the present research in both topic and method, as their study analyzed earthquake crisis management in Sirous neighborhood, Tehran, highlighting the vulnerability of deteriorated urban fabrics due to physical factors. Furthermore, this study’s findings on vulnerability zoning of municipal districts to facilitate identification of critical areas are consistent with those of Karami & Amirian (2018) and Alikhani et al. (2018), who have emphasized the significant role of urban infrastructure networks in shaping seismic vulnerability. Their work underscores the need for reinforcing urban structures, including bridges, tunnels, and facilities; employing earthquake prediction and seismic monitoring systems; and preparing emergency response plans. Coordination among municipal and governmental entities was also emphasized as a critical factor for enhancing earthquake resilience.
This study also corresponds with research conducted by Farajzadeh et al. (2011) and Madadi et al., which attributed central urban vulnerability to deteriorated building fabrics, construction near fault lines, lack of open and green spaces, and similar conditions. Likewise, the present study identifies earthquake vulnerability in Kermanshah as linked to factors such as proximity to faults, socioeconomic conditions of residents, structural resistance, availability of green/open spaces, and population density. These findings highlight the necessity of geological studies to identify seismic risks, reinforcement of deteriorated structures, enforcement of construction codes, evacuation of housing adjacent to fault lines, public training, use of safety equipment, and periodic monitoring and evaluation of building quality and resistance against earthquakes.
In this research, a questionnaire-based fuzzy TOPSIS model was employed to evaluate the vulnerability of eight municipal districts of Kermanshah using more than 45 indicators across four components. Compared to other studies that relied on a limited set of questionnaire-based indicators for weighting, the final output of this study was spatially mapped using GIS software. Based on the model outputs and the findings of this research, and given the significant correlation between vulnerability and fault-line location in Kermanshah, the following recommendations are proposed:
Conclusion
The vulnerability of the eight municipal districts of the Kermanshah metropolis to earthquakes can be attributed to factors such as the proximity of urban constructions to fault lines, the socioeconomic status of residents, structural resistance, the extent of open spaces and emergency land uses, population density, spatial adjacencies, and the presence of deteriorated neighborhoods.
Acknowledgments: None reported by the authors.
Ethical Permission: None reported by the authors.
Conflict of Interest: None reported by the authors.
Authors’ Contributions: Maleki K (First Author): Principal Researcher/Introduction Writer/Statistical Analyst (50%); Pourmohammadi MR (Second Author): Methodologist (20%); Yousefi Shahir H (Third Author): Methodologist/Discussion Writer (20%); Masoudi Asl B (Fourth Author): Assistant Researcher/Statistical Analyst (10%)
Funding: This research was self-funded by the authors.
Urban development and land cover change have been among the most significant human interventions in nature during the twentieth and twenty-first centuries. Currently, more than 70% of the world’s population resides in cities [Maleki et al., 2022]. It is predicted that a substantial portion of population growth will occur in developing countries, where approximately 3.5 billion people already live. This further highlights the importance of addressing and mitigating crises in urban areas [Ziari & Ehsanifard, 2022]. An urban crisis is defined as a sudden event accompanied by extensive human and material losses, requiring immediate actions at the city level to control and contain the crisis and ensure the safety of urban areas under seismic risk [Shams et al., 2011; Alexander, 2002].
Urban vulnerability to natural hazards such as earthquakes can be considered an outcome of human behavior, underscoring the role of planning systems in reducing the destructive impacts of such events [Rashed et al., 2017; Maleki, 2023]. According to forecasts, by 2030, nearly 6 billion out of the projected 8.1 billion global population will be living in cities, with about two-thirds of them residing in metropolitan areas [Alshehabi, 2014]. In Iran, on average, one earthquake with a magnitude of 6 occurs annually, and one with a magnitude of 7 on the Richter scale occurs every decade. The 7.3-magnitude earthquake of November 12, 2017, in Ezgeleh, Kermanshah, is one such example [Maleki et al., 2022]. Therefore, given the importance of crisis management and the necessity of reducing its consequences, it is essential that urban and regional planning integrate civil defense principles as a fundamental element of urban defense planning. Passive defense encompasses measures adopted prior to the occurrence of hazards, facilitating crisis management [Hosseinzadeh Dalir et al., 2012].
Today, coping with hazards is one of the major challenges for most countries [Cutter et al., 2016], as these hazards not only cause loss of life and emotional suffering but also inflict significant damage on local economies [Bazrafshan et al., 2018]. The objective of passive defense projects is to reduce the vulnerability of vital, sensitive, and critical urban facilities and infrastructures under crisis conditions [Soltani et al., 2017]. Earthquake vulnerability can be minimized by adhering to the principles of passive defense. In essence, the core components of passive defense consist of: (1) crisis recognition and identification of potential threats, (2) hazard analysis and assessment of probable risks, (3) foresight and formulation of strategies for hazard mitigation and control, and (4) providing related considerations, provided they align with passive defense principles, with the ultimate goal of deterrence, preservation, and reinforcement of sustainable security within the geographic environment while reducing vulnerability [Pourmohammadi & Maleki, 2021; Pourmohammadi & Maleki, 2016].
Over time, seismic vulnerability in urban areas has increased due to the growing complexity of urban environments [Alavi et al., 2016]. Vulnerability is generally defined as a concept that helps to understand the living conditions under which a hazard can escalate into a disaster [Tapsell et al., 2010]. Both structural and environmental stress factors contribute to urban vulnerability in metropolitan areas. Structural characteristics include city size, density, spatial form, socioeconomic development, and infrastructure, all of which serve as key indicators for evaluating the physical vulnerability of urban areas. Environmental stress factors, on the other hand, are disturbances arising from unexpected events, such as natural disasters, accidents, public health emergencies, and health-related incidents [Chanliang et al., 2011].
Determinants of vulnerability are highly diverse (natural, physical, economic, and social factors) and interact not in isolation but as components of an integrated system [Paton & Johnson, 2001]. If vulnerability is considered as a mathematical function, it represents the predicted degree of damage for each exposed element under a hazard of a certain intensity [Zangiabadei et al., 2008]. For accurate assessment, composite layers and required maps can be generated using GIS [Church & Murray, 2008]. Hazard zoning is a crucial aspect of pre-crisis management processes [Karami & Amirian, 2018]. By evaluating earthquake hazard potential, the required precautions can be implemented to prevent large-scale tragedies and the loss of numerous lives [Khatsü, 2011].
The metropolis of Kermanshah has historically served as a residential, communicational, and demographic hub in western Iran. Considering the city’s demographic capacity and strategic role, analyzing earthquake vulnerability is indispensable. The objective of this study is to assess the vulnerability of municipal districts in the metropolis of Kermanshah from a passive defense perspective and to examine the factors influencing earthquake risks.
Methodology
This study is descriptive in nature and was conducted in 2021 using survey methods and data analysis in the metropolis of Kermanshah. As the most important demographic, political, and physical center of Kermanshah Province, the city covers an urban area of over 11,000 hectares and includes eight municipal districts. According to the latest census in 2016, the population of the city was 946,651. However, in 2017, following the annexation of several villages, the population exceeded one million, and in 2018 the city was officially recognized as a metropolis by the Ministry of Interior [Akbari et al., 2021]. Kermanshah is affected by several active and semi-active faults both within and around the city. The presence of three major fault lines passing through or adjacent to the city has made this urban area seismically vulnerable. Historical earthquakes indicate that factors such as magnitude, shallow depth, timing of occurrence, long duration, and proximity of epicenters to the city have led to severe damages (Maleki, 2018; Maleki, 2020).
The statistical population of the study consisted of experts and scholars specializing in earthquake and passive defense. Based on Cochran’s formula, a sample size of 30 individuals was selected across the eight municipal districts. To assess the reliability of the questionnaire items, Cronbach’s alpha was calculated at 0.925, which, being higher than 0.70, confirmed the instrument’s reliability. Natural, physical, social, and economic components and their indicators were designed in accordance with the objectives and principles of urban passive defense and aligned with prior studies, including academic articles and research projects. Subsequently, a table of influential components contributing to earthquake vulnerability in Kermanshah metropolis was developed based on expert input. Surveys were conducted accordingly, and the identified components were first qualitatively and then quantitatively scored across municipal districts.
The vulnerability of Kermanshah’s eight municipal districts was then assessed across the four domains using the Fuzzy TOPSIS model. Following analysis of the survey results, the output was spatially represented through maps generated with ArcGIS 10.2 software. Finally, the districts were compared in terms of physical development and other relevant indicators.
Analytic Hierarchy Process (AHP) and Eigenvector Method
In the first step, the arithmetic or geometric mean of each row was calculated, and then normalized by column to derive the weight vector. In the second step, the pairwise comparison matrices completed by the participants were analyzed. An approximate method for calculating the eigenvector W involved successive powering of matrix D.
In the third step, multiple judgments were aggregated into a comparison table, with the geometric mean being the preferred method.
The consistency ratio was verified to be below 0.1, confirming acceptable consistency. Ultimately, the sum of each row was divided by the total sum of the matrix to obtain criterion weights.
The final weights confirmed that all components provided a comprehensive representation of the research problem.
Fuzzy TOPSIS Model
To prioritize Kermanshah’s municipal districts in terms of earthquake vulnerability, the Fuzzy TOPSIS model was employed. As a well-established multi-criteria decision-making (MCDM) method, Fuzzy TOPSIS ranks alternatives based on their distance from the positive and negative ideal solutions.
In the first stage, linguistic variables collected via the questionnaire for all indicators and districts were tabulated. Qualitative variables were then converted into quantitative measures for analysis. Subsequently, distances from the negative and positive ideals were computed.
Finally, the closeness coefficient was calculated. Alternatives with higher coefficients (closer to 1) were considered closer to the ideal solution.
Findings
To analyze earthquake vulnerability in Kermanshah, the relevant components and indicators influencing this process were first identified, followed by an assessment of the city’s vulnerability through prioritization of these indicators. Based on prior studies on urban development from the perspective of earthquake risk, natural, social, economic, and physical components are consistently considered. One of the main analytical approaches for evaluating the structure of such components is the eigenvector technique, which estimates the degree of vulnerability. Accordingly, the findings of the descriptive analysis were first presented, followed by inferential results.
It is evident that lower levels of earthquake vulnerability correspond to reduced human and financial losses, and vice versa. Therefore, in order to achieve effective planning and risk assessment, the selection and refinement of relevant components and indicators must be conducted with precision. Within the structural and spatial context of Kermanshah, several factors including proximity to or direct location on fault lines, large urban area, lack of sufficient infrastructure, and others play a significant role in increasing vulnerability. In this study, natural, physical, social, and economic components and their corresponding indicators were identified in alignment with the principles of urban passive defense and in accordance with similar studies. These indicators served as input data for analyzing the vulnerability of Kermanshah’s eight municipal districts.
The influence of each component was then measured using the Shannon entropy model, while expert opinions from specialists in planning and earthquake studies were collected to prepare a qualitative analysis of the components. Finally, experts’ evaluations were quantified into a ranking of the city’s districts in terms of earthquake vulnerability. In this ranking system, the values 9, 7, 5, and 3 corresponded to “very suitable,” “suitable,” “unsuitable,” and “very unsuitable,” respectively. After scoring by the experts, the vulnerability status of Kermanshah’s districts was analyzed using the fuzzy TOPSIS model. In the next step, the output maps were analyzed to compare districts in terms of physical development and other factors, which yielded practical insights such as identifying optimal directions for future development and reducing the critical impacts of earthquakes by recognizing highly vulnerable areas.
Among the components, the physical dimension held less significance compared to others, while the social and economic dimensions also showed lower weight, whereas the natural component was found to have the highest influence. In the eigenvector analysis, a micro epsilon vector value was applied, the final weights were calculated, and the consistency ratio was found to be below 0.1, confirming the acceptability of the results (since a higher ratio would have required revision). The eigenvector analysis demonstrated that the natural component ranked first in importance, followed by the physical component in second place, while the social and economic components ranked lower.
The selected components were then evaluated across Kermanshah’s eight municipal districts to assess their suitability. The results indicated that most districts were “suitable” in terms of natural and economic components. For the social component, districts 1 and 8 were evaluated as “very suitable,” whereas districts 2, 3, 6, and 7 were found “unsuitable,” requiring attention and improvement. Regarding the physical component, districts 1, 4, and 6 were deemed “very suitable,” while districts 2, 3, 5, 7, and 8 were considered “unsuitable,” calling for corrective measures. These differences highlight the need for context-specific decisions to improve earthquake vulnerability management.
The fuzzy evaluation results for each component and district were calculated. Subsequently, normalization was carried out using the Euclidean norm method. The square root of the sum of squared lower bounds for the eight districts was calculated for each index, and the same was done for the middle and upper bounds. These values were then divided by the respective bounds of the Euclidean norm, resulting in a normalized matrix. The indicator weights, obtained through eigenvector or Shannon entropy methods, were expressed in triangular form. The distances of each option from the positive and negative ideal solutions were then calculated using the corresponding formulas.
The relative closeness of each option to the ideal solution determined their ranking. The results revealed the following ranking: District 8 in first place, District 4 in second, District 5 in third, District 1 in fourth, Districts 2 and 3 in fifth, District 6 in sixth, and District 7 in seventh place.
According to the results of the fuzzy TOPSIS model, districts 8, 4, 5, and 1 had the lowest levels of vulnerability, while the other districts (7, 6, 3, and 2) demonstrated relatively unfavorable conditions. However, in general, the open and green spaces of districts 5 and 2, and to some extent district 6, were found to be in relatively poor condition.
Discussion
The findings indicate that what often transforms an earthquake hazard into a catastrophe in urban areas is the inadequacy of urban planning and management. Therefore, by applying passive defense principles consistent with urban planning and civil defense considerations such as land-use planning, decentralization, and restricting development in proximity to high-risk or hazardous facilities the vulnerability of cities to earthquakes can be significantly reduced.
Moreover, assessing earthquake hazard potential lowers the level of uncertainty and necessary precautions, thereby preventing large-scale tragedies and loss of life [Khatsü, 2011]. Given the demographic significance of the Kermanshah metropolis, analyzing its earthquake vulnerability is inevitable. The objective of this study was to evaluate the vulnerability of Kermanshah’s municipal districts from the perspective of passive defense and to examine influencing factors. The results suggest that in formulating preparedness and crisis management strategies, initial attention must be given to variables such as fault line proximity and land slope percentage. Next, factors such as proximity to major streets, fire stations, healthcare centers, and land use, as well as social aspects (e.g., household and population density), should be considered. Finally, economic components such as household dependency ratios, active/inactive population densities, and unemployment rates reveal their importance in maintaining socioeconomic resilience following an earthquake, thereby enhancing the preparedness of Kermanshah to cope more effectively with seismic hazards [Ebrahimi et al., 2016].
The ranking of Kermanshah’s municipal districts in terms of earthquake vulnerability using the fuzzy TOPSIS model showed that higher-ranked districts in terms of risk require urgent attention and immediate crisis management measures. Lower-ranked districts, while less exposed, still require planning to strengthen resilience against seismic hazards. Such analyses provide a foundation for developing strategies and managerial decisions in preparedness and response planning.
The results of this study are aligned with those of Isalu et al. (2015), who have assessed the physical vulnerability of urban districts in Tehran’s District 1 using the IHWP hierarchical analysis method in ArcGIS. Similarly, the work of Mohammadpour et al. (2016) corresponds with the present research in both topic and method, as their study analyzed earthquake crisis management in Sirous neighborhood, Tehran, highlighting the vulnerability of deteriorated urban fabrics due to physical factors. Furthermore, this study’s findings on vulnerability zoning of municipal districts to facilitate identification of critical areas are consistent with those of Karami & Amirian (2018) and Alikhani et al. (2018), who have emphasized the significant role of urban infrastructure networks in shaping seismic vulnerability. Their work underscores the need for reinforcing urban structures, including bridges, tunnels, and facilities; employing earthquake prediction and seismic monitoring systems; and preparing emergency response plans. Coordination among municipal and governmental entities was also emphasized as a critical factor for enhancing earthquake resilience.
This study also corresponds with research conducted by Farajzadeh et al. (2011) and Madadi et al., which attributed central urban vulnerability to deteriorated building fabrics, construction near fault lines, lack of open and green spaces, and similar conditions. Likewise, the present study identifies earthquake vulnerability in Kermanshah as linked to factors such as proximity to faults, socioeconomic conditions of residents, structural resistance, availability of green/open spaces, and population density. These findings highlight the necessity of geological studies to identify seismic risks, reinforcement of deteriorated structures, enforcement of construction codes, evacuation of housing adjacent to fault lines, public training, use of safety equipment, and periodic monitoring and evaluation of building quality and resistance against earthquakes.
In this research, a questionnaire-based fuzzy TOPSIS model was employed to evaluate the vulnerability of eight municipal districts of Kermanshah using more than 45 indicators across four components. Compared to other studies that relied on a limited set of questionnaire-based indicators for weighting, the final output of this study was spatially mapped using GIS software. Based on the model outputs and the findings of this research, and given the significant correlation between vulnerability and fault-line location in Kermanshah, the following recommendations are proposed:
- Revision of the comprehensive plan with priority given to renewal and improvement of deteriorated urban fabrics and vulnerable areas, emphasizing the inclusion of passive defense appendices in urban development plans.
- Relocation or conversion of conflicting land uses, utilization of brownfield and recycled lands, development of flexible and multi-functional land uses, and avoidance of fault-adjacent areas wherever possible.
- Consideration of the city’s morphological pattern, acknowledging that the current radial-linear structure may not be the optimal model. Instead, a grid-like internal structure may provide a more appropriate development framework for Kermanshah, while limited expansion in the western sector of the city should also be integrated into future development strategies
Conclusion
The vulnerability of the eight municipal districts of the Kermanshah metropolis to earthquakes can be attributed to factors such as the proximity of urban constructions to fault lines, the socioeconomic status of residents, structural resistance, the extent of open spaces and emergency land uses, population density, spatial adjacencies, and the presence of deteriorated neighborhoods.
Acknowledgments: None reported by the authors.
Ethical Permission: None reported by the authors.
Conflict of Interest: None reported by the authors.
Authors’ Contributions: Maleki K (First Author): Principal Researcher/Introduction Writer/Statistical Analyst (50%); Pourmohammadi MR (Second Author): Methodologist (20%); Yousefi Shahir H (Third Author): Methodologist/Discussion Writer (20%); Masoudi Asl B (Fourth Author): Assistant Researcher/Statistical Analyst (10%)
Funding: This research was self-funded by the authors.
References
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