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Volume 37, Issue 3 (2022)
GeoRes 2022, 37(3): 339-349 |
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Received: 2022/06/5 | Accepted: 2022/08/1 | Published: 2022/07/1
Received: 2022/06/5 | Accepted: 2022/08/1 | Published: 2022/07/1
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Behrouzi H, Zand Moghadam M, Kamyabi S. Modeling the Resilience of the City Against Natural Hazards with Emphasis on Landslide (Case study: Ghaemshahr City). GeoRes 2022; 37 (3) :339-349
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1- Department of Geography and Urban Planning, Semnan Branch, Islamic Azad University, Semnan, Iran
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Introduction
Resilience refers to positive adaptation in response to adverse conditions [Jahed Motlagh et al., 2015] and plays a crucial role in returning to an initial state of equilibrium or achieving a higher level of stability, thereby enabling positive and successful adaptation in life [Mehraban & Livarjani, 2018]. Resilience is not merely the passive resistance to harm or threatening conditions; rather, a resilient individual actively engages with and constructs their surrounding environment [Mashayekhi & Mohammadi, 2014]. It represents a person’s capacity to maintain biological, psychological, and spiritual balance when faced with hazardous conditions [Honarmand Zadeh & Sajjadian, 2017]. Today, resilience is widely applied across various fields, especially in disaster management [Eshghei & Nazmfar, 2019]. The disasters occurring in recent years indicate that societies and individuals have become increasingly vulnerable, and risks have escalated correspondingly [Parvizian & Maleki, 2022]. Nevertheless, risk and vulnerability reduction are often overlooked until after disasters occur [Firouzpour, 2021]. In a context of growing risks and uncertainties, resilience is introduced as a concept for confronting disruptions, shocks, and changes [Ghasemi Jobneh et al., 2016]. It also represents the capacity to withstand stress and catastrophic events. Psychologists have consistently sought to enhance this human capability to adapt and overcome danger and hardship [Heidarifar et al., 2020]. Resilience has been described as the degree of disturbance that a system can absorb, through changes in its parameters and processes that control its behavior, before its structure transforms into a fundamentally different one [Jazayeri et al., 2018].
The term “resilience” is equivalent to resiliency, which in dictionaries refers to elasticity, recoverability, and the ability to rebound [Heydari et al., 2022]. It is worth noting that the introduction of resilience into urban studies, crisis management, organizational contexts, and even everyday life is akin to the emergence of a new cultural paradigm [Sadeghi Roush et al., 2014]. Although considerable attention has been devoted to resilience in Iran and worldwide, only a limited number of systematically formulated regulations concerning organizational resilience currently exist [Jahed Motlagh et al., 2015]. Resilience has been defined as “a process, ability, or outcome of successful adaptation despite threatening conditions,” playing a significant role in coping with life’s stressors, threats, and their adverse consequences [Mehraban & Livarjani, 2018], and representing a form of self-restoration associated with positive emotional, affective, and cognitive outcomes [Ahmadzadeh & Aminzadeh, 2020].
The pathway to resilience develops through working with and addressing the effects of stress and traumatic events. Enhancing resilience contributes to individuals’ growth in acquiring improved self-management skills, more advanced thinking, and greater knowledge [Ghasemi Jobneh et al., 2016]. Therefore, determining resilience levels and identifying the factors that help maintain calmness and raise the tolerance threshold among managers and staff can improve the success and performance of communities during crises [Pakandish et al., 2019]. Experience indicates that the most critical factor in pre-crisis preparedness is identifying levels of vulnerability, prioritizing them, and determining solutions for preventing and controlling potential hazards [Sanaei & Taherimehr, 2015]. Urban resilience is a development strategy that applies across geographical, social, economic, and other domains with potential for growth [Maleki et al., 2017]. Such thinking undoubtedly adds a new and persuasive dimension to planning policies and introduces a new outlook for urban planning [Zand Moghadam & Molaie, 2020].
Numerous studies have examined resilience. However, considering the objective of the present research, assessing urban resilience against natural hazards, the studies that have directly addressed this topic are highlighted here. Salehipour et al. [2021] have evaluated the vulnerability and resilience of Razan city to earthquakes and found that none of the city’s neighborhoods exhibited high or very high levels of preparedness or resilience. Weak performance of managerial and institutional structures and the limited economic capacity of citizens to recover after earthquakes were identified as key factors reducing Razan’s resilience. Mododi et al. [2020] have concluded that the resilience of the studied villages was at a moderate level, although resilience varied among them. They emphasized the importance of implementing programs to improve economic conditions, such as economic diversification, and offering social empowerment courses. Bagheranjad and Azizi [2020] have stated that the spatial distribution of resilience and its dimensions across Tehran metropolis indicated that infrastructural factors had the greatest negative effect on neighborhoods with poor resilience conditions. Ahmadzadeh and Aminzadeh [2020] noted that natural disasters remain a major challenge to achieving sustainable development. The prevailing approaches in disaster and urban management have primarily focused on mitigation. The literature on hazards has shifted paradigmatically from “hazard assessment” to “vulnerability analysis.” Maleki et al. [2017] have emphasized that earthquakes pose a major threat to cities and that reducing damage, improving crisis management, and enhancing urban resilience are necessary. They concluded that significant differences exist among the neighborhoods of Izeh city in terms of social resilience. Zand Moghadam et al. [2019] have highlighted the region’s high vulnerability and stressed that assessing earthquake-induced vulnerability across various indicators is crucial for urban planning. Parker [2020] argues that the concept of resilience has gained widespread attention and now dominates thinking around risk management, including environmental hazards, concluding that resilience is a concept that goes beyond reductionist approaches and embraces adaptation, change, and transformation. Summers et al. [2018] note that natural disasters often impose substantial and long-term stress on financial, social, and environmental systems. Bakkensen et al. [2017] have suggested that given the persistent and serious threats of natural disasters worldwide, regional policy development or investments in resilience projects should prioritize high-impact indicators.
Therefore, undertaking actions to prepare for natural hazards and understanding strengths and weaknesses is imperative for establishing a safer development trajectory in the future. Qaemshahr city, given its regional significance, must enhance its resilience as soon as possible to address forthcoming challenges and safeguard its valuable heritage. Examining resilience is a crucial requirement for urban planners, enabling officials and planners to design future strategies and plans aimed at minimizing hazard-related problems and establishing feasible solutions to develop resilient cities. Such efforts contribute to addressing the challenges facing the study area through a scientifically grounded approach. Accordingly, the aim of the present research was to model the level of urban resilience against natural hazards, with an emphasis on landslides under critical conditions.
Methodology
The city of Qaemshahr is located between 36°21' to 36°38' north latitude and 52°43' to 53°03' east longitude from the Greenwich meridian. According to the latest census conducted by the Statistical Center of Iran in 2016, the city has a population of 204,953 and comprises 68,407 households. The area of the city is 458 square kilometers. Due to its geographical location, being situated on the main route leading to the holy city of Mashhad on one hand, and serving as one of the most important transportation corridors connecting northern Iran to the capital on the other, Qaemshahr has significant potential for commercial and tourism functions at both regional and national levels [Janbaz Ghobadi et al., 2011].
This research was applied in purpose and descriptive–analytical in method, relying on library studies and urban maps for analysis. Data collection was carried out through documentary sources as well as field observations (visits to vulnerable areas). To assess and evaluate the city’s resilience to natural hazards, the influential indicators and factors within the study area were first identified. Subsequently, the required spatial data were collected, and the relevant maps were produced in a GIS environment. These maps were then analyzed using the Analytical Hierarchy Process (AHP), and finally, the composite resilience map was generated. To measure the city’s resilience across four dimensions of physical–spatial, economic, social, and institutional, 26 indicators and 42 sub-indicators were employed. The selection of these indicators and sub-indicators was based on four criteria: data availability, existing research background, relevance according to the resilience literature, and the analysis of urban maps affecting resilience to landslides.
To evaluate Qaemshahr’s resilience to natural hazards with an emphasis on landslides, the identified indicators and criteria were incorporated into GIS maps. After weighting these maps using the AHP model, they were further processed using a Genetic Algorithm (GA), coded in MATLAB. From this analysis, 40 locations within the city that exhibited the highest levels of resilience, those associated with the greatest number of indicators, were identified, and ultimately, five priority locations were determined. Given that GA is a population-based optimization method and the research problem is discrete in nature, a comparable population-based method was employed to validate the GA results. For this purpose, Particle Swarm Optimization (PSO) was used, and its outputs confirmed the results obtained from the GA.
Findings
Analysis of Indicators and Determination of the Importance of Urban Resilience Dimensions Against Natural Hazards
The resilience of Qaemshahr was examined across four dimensions, each consisting of several indicators and sub-indicators. The results obtained from the analysis of the relevant data were presented according to these four categories.
To identify and assess high-risk zones, the Analytical Hierarchy Process (AHP) was utilized. The required decision-making criteria were first determined, including land-use density, distance from faults, slope classes, buffers of green spaces, sports areas and parks, population density, street networks, deteriorated urban fabric, physical density, and other relevant factors. Based on these criteria, the necessary thematic maps were produced in a GIS environment. Each criterion was then weighted, with the weight values reflecting the relative importance of each criterion compared with the others.
For producing the hazard-susceptibility and resilience maps related to landslides, specific functional maps were used. The required weighting procedures were applied, and the final output map was generated with a spectrum ranging from highly resilient zones to areas with the lowest levels of resilience.
Analysis of Urban Resilience Indicators Using the Genetic Algorithm (GA)
To analyze the structure of urban resilience, four main dimensions of physical–spatial, economic, social, and institutional, were identified based on authoritative scientific literature. Since each indicator contained multiple sub-indicators, some could be quantitatively evaluated while others were inherently qualitative. Qualitative elements were converted into quantitative values according to relevant standards, and all elements were scored on a scale from 1 to 5 (1=very poor, 2=poor, 3=moderate, 4=good, and 5=very good). Sub-indicators shared across the four dimensions were extracted and, after quantification, were scored accordingly.
Subsequently, forty highly resilient urban points, identified through GIS-based results, were assessed and analyzed using the existing indicators. To ensure logical outcomes in the GA, the urban points were examined, and multiple iterations of the algorithm showed that the best results were obtained when the analysis focused on points numbered approximately between 15 and 35. In other words, among the forty points identified, those within this numerical range received the highest number of indicators. Based on this finding, an evaluation matrix was developed to examine how chromosome rotations, combinations, and mutations affected the results.
Interpretation of the generated charts showed that urban point number 35 generally performed better than points 15 and 25 in most analyses. Additional evaluations were conducted on this point using different combinations and mutation rates to determine which configuration yielded the best performance. These assessments demonstrated that the algorithm displayed stronger convergence under specific mutation and crossover values and reached optimal solutions more rapidly.
Through analysis of the GIS-derived maps related to natural hazards and the results produced by the algorithm, the forty urban points were ranked based on their resilience scores. To optimize interpretation, the five top-ranking locations were identified and selected for further analysis.
Use of the Particle Swarm Optimization (PSO) Model to Compare GA Results
Analysis of the PSO output confirmed the validity of the patterns generated by the GA. In other words, the behavior of the genetic algorithm in identifying the most resilient points, based on spatial data extracted from GIS, was consistent and reliable, and the comparative results validated this outcome. The final resilience map for landslide susceptibility in the study area was produced using GIS models and analyzed alongside the numerical and graphical outputs generated by the GA.
Discussion
The present study was conducted with the aim of assessing urban resilience in the city of Qaemshahr. As one of the strategic urban centers of Mazandaran Province, Qaemshahr is highly vulnerable to natural hazards, particularly landslides due to its specific geographical position and environmental conditions. Accordingly, the assessment of urban resilience constitutes a fundamental requirement for effective urban management. To evaluate resilience within the study area, various influential indicators were employed using the AHP, GA, and PSO models.
The findings indicated that physical–spatial factors, such as topography, urban form and fabric, accessibility, land use, the presence of hazardous facilities, built density, and the street network exert a greater influence than other factors. Therefore, in the context of urban resilience, attention to physical–spatial components and parameters was of critical importance.
Within the economic structure, a set of key factors and components collectively influenced the level of urban resilience. Among the most significant economic determinants were land value, construction costs, the capacity for compensating losses, and land ownership. Other parameters, such as economic capital, household income, and employment and unemployment rates were more susceptible to external influences. Thus, it can be argued that economic factors possess a high level of influence across the city.
The social structure constituted one of the most crucial determinants of urban resilience. Multiple components shaped this structure, including social capital, awareness of natural hazards, gender and age composition, population density, educational attainment, and the cohesion of employee participation. Analysis of the social factors affecting resilience showed that social capital and hazard awareness exert the highest levels of influence, whereas gender and age composition and population density exhibited the greatest degree of susceptibility. Notably, training in disaster preparedness was both influential and influenced by other parameters.
Institutional planning and attention to institutional structures had become essential pathways toward sustainable development in the context of urban resilience. The primary reason for this was linked to the implementation stage of planning. Analysis of institutional factors affecting resilience demonstrated that compliance with construction regulations, technical and operational preparedness during hazard events, and the availability of municipal and emergency response resources were among the most influential components. Other factors, such as inter-agency coordination and collaboration between citizens and organizations, were more susceptible to external influences. Based on this analysis, strengthening coordination and improving infrastructural indicators within the institutional structure can significantly enhance urban resilience.
In recent years, natural hazards have caused extensive physical damage to urban areas, thereby elevating the importance of resilience as a key concept in disaster management aimed at reducing the impacts of such events. Among the various dimensions of resilience, physical resilience was particularly significant, as it provides a basis for assessing the condition of communities in terms of physical and geographical characteristics that influence their vulnerability during hazard occurrences. Historical evidence indicates that several locations within the study area have experienced both human and financial losses as a result of such natural hazards.
It should be noted that the results of the present study are consistent with the findings of Modoodi et al. (2020) and Ahmadzadeh and Aminzadeh (2020), which are conducted in the city of Shiraz and District 9 of Mashhad Municipality, respectively.
Based on the results and the indicators examined for assessing urban resilience in Qaemshahr, several practical recommendations can be proposed: Observance of adequate street widths and improvement of access networks, particularly in deteriorated urban fabrics; maintaining appropriate buffer zones around hazardous land uses; ensuring balanced distribution of critical infrastructure; adhering to technical construction standards and regularly updating building codes; promoting public awareness of disaster management through schools and mass media; ensuring public access to lifelines and open spaces; and addressing building density, population congestion, and the conditions of deteriorated and informal settlements, particularly in villages recently annexed to the city.
The limitations of this study included restricted access to primary international sources, limited cooperation from respondents and practitioners, insufficient prior studies on resilience within the city, and the lack of comprehensive and reliable local data.
Conclusion
The city of Qaemshahr, as the study area, exhibits an overall level of urban resilience that falls below the optimal threshold. The degree to which different districts benefit from resilience-related criteria is neither uniform nor evenly distributed, and significant variations exist among them in terms of resilience indicators. These disparities highlight the necessity for urban managers to prioritize strategies aimed at enhancing resilience across the city.
Acknowledgments: The authors would like to express their sincere appreciation to all officials of the Faculty of Humanities at Islamic Azad University, Semnan Branch, for their support.
Ethical Permission: This article has not been published in any national or international journal to date.
Conflict of Interest: The present article is derived from the PhD dissertation of Mr. Hamzeh Behroozi, supervised by Dr. Mohammadreza Zand Moghaddam, entitled “Modeling Urban Resilience Against Natural Hazards (Case Study: Qaemshahr Urban Area).”
Author Contributions: Behrouzi H (First Author), Main researcher /Discussion Writer (50%); Zand Moghadam MR (Second Author), Methodologist/Discussion Writer (25%); Kamyabi S (Third Author), Assistant Researcher (25%)
Funding: All expenses were covered by Hamzeh Behroozi, and no organization served as a financial sponsor
Resilience refers to positive adaptation in response to adverse conditions [Jahed Motlagh et al., 2015] and plays a crucial role in returning to an initial state of equilibrium or achieving a higher level of stability, thereby enabling positive and successful adaptation in life [Mehraban & Livarjani, 2018]. Resilience is not merely the passive resistance to harm or threatening conditions; rather, a resilient individual actively engages with and constructs their surrounding environment [Mashayekhi & Mohammadi, 2014]. It represents a person’s capacity to maintain biological, psychological, and spiritual balance when faced with hazardous conditions [Honarmand Zadeh & Sajjadian, 2017]. Today, resilience is widely applied across various fields, especially in disaster management [Eshghei & Nazmfar, 2019]. The disasters occurring in recent years indicate that societies and individuals have become increasingly vulnerable, and risks have escalated correspondingly [Parvizian & Maleki, 2022]. Nevertheless, risk and vulnerability reduction are often overlooked until after disasters occur [Firouzpour, 2021]. In a context of growing risks and uncertainties, resilience is introduced as a concept for confronting disruptions, shocks, and changes [Ghasemi Jobneh et al., 2016]. It also represents the capacity to withstand stress and catastrophic events. Psychologists have consistently sought to enhance this human capability to adapt and overcome danger and hardship [Heidarifar et al., 2020]. Resilience has been described as the degree of disturbance that a system can absorb, through changes in its parameters and processes that control its behavior, before its structure transforms into a fundamentally different one [Jazayeri et al., 2018].
The term “resilience” is equivalent to resiliency, which in dictionaries refers to elasticity, recoverability, and the ability to rebound [Heydari et al., 2022]. It is worth noting that the introduction of resilience into urban studies, crisis management, organizational contexts, and even everyday life is akin to the emergence of a new cultural paradigm [Sadeghi Roush et al., 2014]. Although considerable attention has been devoted to resilience in Iran and worldwide, only a limited number of systematically formulated regulations concerning organizational resilience currently exist [Jahed Motlagh et al., 2015]. Resilience has been defined as “a process, ability, or outcome of successful adaptation despite threatening conditions,” playing a significant role in coping with life’s stressors, threats, and their adverse consequences [Mehraban & Livarjani, 2018], and representing a form of self-restoration associated with positive emotional, affective, and cognitive outcomes [Ahmadzadeh & Aminzadeh, 2020].
The pathway to resilience develops through working with and addressing the effects of stress and traumatic events. Enhancing resilience contributes to individuals’ growth in acquiring improved self-management skills, more advanced thinking, and greater knowledge [Ghasemi Jobneh et al., 2016]. Therefore, determining resilience levels and identifying the factors that help maintain calmness and raise the tolerance threshold among managers and staff can improve the success and performance of communities during crises [Pakandish et al., 2019]. Experience indicates that the most critical factor in pre-crisis preparedness is identifying levels of vulnerability, prioritizing them, and determining solutions for preventing and controlling potential hazards [Sanaei & Taherimehr, 2015]. Urban resilience is a development strategy that applies across geographical, social, economic, and other domains with potential for growth [Maleki et al., 2017]. Such thinking undoubtedly adds a new and persuasive dimension to planning policies and introduces a new outlook for urban planning [Zand Moghadam & Molaie, 2020].
Numerous studies have examined resilience. However, considering the objective of the present research, assessing urban resilience against natural hazards, the studies that have directly addressed this topic are highlighted here. Salehipour et al. [2021] have evaluated the vulnerability and resilience of Razan city to earthquakes and found that none of the city’s neighborhoods exhibited high or very high levels of preparedness or resilience. Weak performance of managerial and institutional structures and the limited economic capacity of citizens to recover after earthquakes were identified as key factors reducing Razan’s resilience. Mododi et al. [2020] have concluded that the resilience of the studied villages was at a moderate level, although resilience varied among them. They emphasized the importance of implementing programs to improve economic conditions, such as economic diversification, and offering social empowerment courses. Bagheranjad and Azizi [2020] have stated that the spatial distribution of resilience and its dimensions across Tehran metropolis indicated that infrastructural factors had the greatest negative effect on neighborhoods with poor resilience conditions. Ahmadzadeh and Aminzadeh [2020] noted that natural disasters remain a major challenge to achieving sustainable development. The prevailing approaches in disaster and urban management have primarily focused on mitigation. The literature on hazards has shifted paradigmatically from “hazard assessment” to “vulnerability analysis.” Maleki et al. [2017] have emphasized that earthquakes pose a major threat to cities and that reducing damage, improving crisis management, and enhancing urban resilience are necessary. They concluded that significant differences exist among the neighborhoods of Izeh city in terms of social resilience. Zand Moghadam et al. [2019] have highlighted the region’s high vulnerability and stressed that assessing earthquake-induced vulnerability across various indicators is crucial for urban planning. Parker [2020] argues that the concept of resilience has gained widespread attention and now dominates thinking around risk management, including environmental hazards, concluding that resilience is a concept that goes beyond reductionist approaches and embraces adaptation, change, and transformation. Summers et al. [2018] note that natural disasters often impose substantial and long-term stress on financial, social, and environmental systems. Bakkensen et al. [2017] have suggested that given the persistent and serious threats of natural disasters worldwide, regional policy development or investments in resilience projects should prioritize high-impact indicators.
Therefore, undertaking actions to prepare for natural hazards and understanding strengths and weaknesses is imperative for establishing a safer development trajectory in the future. Qaemshahr city, given its regional significance, must enhance its resilience as soon as possible to address forthcoming challenges and safeguard its valuable heritage. Examining resilience is a crucial requirement for urban planners, enabling officials and planners to design future strategies and plans aimed at minimizing hazard-related problems and establishing feasible solutions to develop resilient cities. Such efforts contribute to addressing the challenges facing the study area through a scientifically grounded approach. Accordingly, the aim of the present research was to model the level of urban resilience against natural hazards, with an emphasis on landslides under critical conditions.
Methodology
The city of Qaemshahr is located between 36°21' to 36°38' north latitude and 52°43' to 53°03' east longitude from the Greenwich meridian. According to the latest census conducted by the Statistical Center of Iran in 2016, the city has a population of 204,953 and comprises 68,407 households. The area of the city is 458 square kilometers. Due to its geographical location, being situated on the main route leading to the holy city of Mashhad on one hand, and serving as one of the most important transportation corridors connecting northern Iran to the capital on the other, Qaemshahr has significant potential for commercial and tourism functions at both regional and national levels [Janbaz Ghobadi et al., 2011].
This research was applied in purpose and descriptive–analytical in method, relying on library studies and urban maps for analysis. Data collection was carried out through documentary sources as well as field observations (visits to vulnerable areas). To assess and evaluate the city’s resilience to natural hazards, the influential indicators and factors within the study area were first identified. Subsequently, the required spatial data were collected, and the relevant maps were produced in a GIS environment. These maps were then analyzed using the Analytical Hierarchy Process (AHP), and finally, the composite resilience map was generated. To measure the city’s resilience across four dimensions of physical–spatial, economic, social, and institutional, 26 indicators and 42 sub-indicators were employed. The selection of these indicators and sub-indicators was based on four criteria: data availability, existing research background, relevance according to the resilience literature, and the analysis of urban maps affecting resilience to landslides.
To evaluate Qaemshahr’s resilience to natural hazards with an emphasis on landslides, the identified indicators and criteria were incorporated into GIS maps. After weighting these maps using the AHP model, they were further processed using a Genetic Algorithm (GA), coded in MATLAB. From this analysis, 40 locations within the city that exhibited the highest levels of resilience, those associated with the greatest number of indicators, were identified, and ultimately, five priority locations were determined. Given that GA is a population-based optimization method and the research problem is discrete in nature, a comparable population-based method was employed to validate the GA results. For this purpose, Particle Swarm Optimization (PSO) was used, and its outputs confirmed the results obtained from the GA.
Findings
Analysis of Indicators and Determination of the Importance of Urban Resilience Dimensions Against Natural Hazards
The resilience of Qaemshahr was examined across four dimensions, each consisting of several indicators and sub-indicators. The results obtained from the analysis of the relevant data were presented according to these four categories.
To identify and assess high-risk zones, the Analytical Hierarchy Process (AHP) was utilized. The required decision-making criteria were first determined, including land-use density, distance from faults, slope classes, buffers of green spaces, sports areas and parks, population density, street networks, deteriorated urban fabric, physical density, and other relevant factors. Based on these criteria, the necessary thematic maps were produced in a GIS environment. Each criterion was then weighted, with the weight values reflecting the relative importance of each criterion compared with the others.
For producing the hazard-susceptibility and resilience maps related to landslides, specific functional maps were used. The required weighting procedures were applied, and the final output map was generated with a spectrum ranging from highly resilient zones to areas with the lowest levels of resilience.
Analysis of Urban Resilience Indicators Using the Genetic Algorithm (GA)
To analyze the structure of urban resilience, four main dimensions of physical–spatial, economic, social, and institutional, were identified based on authoritative scientific literature. Since each indicator contained multiple sub-indicators, some could be quantitatively evaluated while others were inherently qualitative. Qualitative elements were converted into quantitative values according to relevant standards, and all elements were scored on a scale from 1 to 5 (1=very poor, 2=poor, 3=moderate, 4=good, and 5=very good). Sub-indicators shared across the four dimensions were extracted and, after quantification, were scored accordingly.
Subsequently, forty highly resilient urban points, identified through GIS-based results, were assessed and analyzed using the existing indicators. To ensure logical outcomes in the GA, the urban points were examined, and multiple iterations of the algorithm showed that the best results were obtained when the analysis focused on points numbered approximately between 15 and 35. In other words, among the forty points identified, those within this numerical range received the highest number of indicators. Based on this finding, an evaluation matrix was developed to examine how chromosome rotations, combinations, and mutations affected the results.
Interpretation of the generated charts showed that urban point number 35 generally performed better than points 15 and 25 in most analyses. Additional evaluations were conducted on this point using different combinations and mutation rates to determine which configuration yielded the best performance. These assessments demonstrated that the algorithm displayed stronger convergence under specific mutation and crossover values and reached optimal solutions more rapidly.
Through analysis of the GIS-derived maps related to natural hazards and the results produced by the algorithm, the forty urban points were ranked based on their resilience scores. To optimize interpretation, the five top-ranking locations were identified and selected for further analysis.
Use of the Particle Swarm Optimization (PSO) Model to Compare GA Results
Analysis of the PSO output confirmed the validity of the patterns generated by the GA. In other words, the behavior of the genetic algorithm in identifying the most resilient points, based on spatial data extracted from GIS, was consistent and reliable, and the comparative results validated this outcome. The final resilience map for landslide susceptibility in the study area was produced using GIS models and analyzed alongside the numerical and graphical outputs generated by the GA.
Discussion
The present study was conducted with the aim of assessing urban resilience in the city of Qaemshahr. As one of the strategic urban centers of Mazandaran Province, Qaemshahr is highly vulnerable to natural hazards, particularly landslides due to its specific geographical position and environmental conditions. Accordingly, the assessment of urban resilience constitutes a fundamental requirement for effective urban management. To evaluate resilience within the study area, various influential indicators were employed using the AHP, GA, and PSO models.
The findings indicated that physical–spatial factors, such as topography, urban form and fabric, accessibility, land use, the presence of hazardous facilities, built density, and the street network exert a greater influence than other factors. Therefore, in the context of urban resilience, attention to physical–spatial components and parameters was of critical importance.
Within the economic structure, a set of key factors and components collectively influenced the level of urban resilience. Among the most significant economic determinants were land value, construction costs, the capacity for compensating losses, and land ownership. Other parameters, such as economic capital, household income, and employment and unemployment rates were more susceptible to external influences. Thus, it can be argued that economic factors possess a high level of influence across the city.
The social structure constituted one of the most crucial determinants of urban resilience. Multiple components shaped this structure, including social capital, awareness of natural hazards, gender and age composition, population density, educational attainment, and the cohesion of employee participation. Analysis of the social factors affecting resilience showed that social capital and hazard awareness exert the highest levels of influence, whereas gender and age composition and population density exhibited the greatest degree of susceptibility. Notably, training in disaster preparedness was both influential and influenced by other parameters.
Institutional planning and attention to institutional structures had become essential pathways toward sustainable development in the context of urban resilience. The primary reason for this was linked to the implementation stage of planning. Analysis of institutional factors affecting resilience demonstrated that compliance with construction regulations, technical and operational preparedness during hazard events, and the availability of municipal and emergency response resources were among the most influential components. Other factors, such as inter-agency coordination and collaboration between citizens and organizations, were more susceptible to external influences. Based on this analysis, strengthening coordination and improving infrastructural indicators within the institutional structure can significantly enhance urban resilience.
In recent years, natural hazards have caused extensive physical damage to urban areas, thereby elevating the importance of resilience as a key concept in disaster management aimed at reducing the impacts of such events. Among the various dimensions of resilience, physical resilience was particularly significant, as it provides a basis for assessing the condition of communities in terms of physical and geographical characteristics that influence their vulnerability during hazard occurrences. Historical evidence indicates that several locations within the study area have experienced both human and financial losses as a result of such natural hazards.
It should be noted that the results of the present study are consistent with the findings of Modoodi et al. (2020) and Ahmadzadeh and Aminzadeh (2020), which are conducted in the city of Shiraz and District 9 of Mashhad Municipality, respectively.
Based on the results and the indicators examined for assessing urban resilience in Qaemshahr, several practical recommendations can be proposed: Observance of adequate street widths and improvement of access networks, particularly in deteriorated urban fabrics; maintaining appropriate buffer zones around hazardous land uses; ensuring balanced distribution of critical infrastructure; adhering to technical construction standards and regularly updating building codes; promoting public awareness of disaster management through schools and mass media; ensuring public access to lifelines and open spaces; and addressing building density, population congestion, and the conditions of deteriorated and informal settlements, particularly in villages recently annexed to the city.
The limitations of this study included restricted access to primary international sources, limited cooperation from respondents and practitioners, insufficient prior studies on resilience within the city, and the lack of comprehensive and reliable local data.
Conclusion
The city of Qaemshahr, as the study area, exhibits an overall level of urban resilience that falls below the optimal threshold. The degree to which different districts benefit from resilience-related criteria is neither uniform nor evenly distributed, and significant variations exist among them in terms of resilience indicators. These disparities highlight the necessity for urban managers to prioritize strategies aimed at enhancing resilience across the city.
Acknowledgments: The authors would like to express their sincere appreciation to all officials of the Faculty of Humanities at Islamic Azad University, Semnan Branch, for their support.
Ethical Permission: This article has not been published in any national or international journal to date.
Conflict of Interest: The present article is derived from the PhD dissertation of Mr. Hamzeh Behroozi, supervised by Dr. Mohammadreza Zand Moghaddam, entitled “Modeling Urban Resilience Against Natural Hazards (Case Study: Qaemshahr Urban Area).”
Author Contributions: Behrouzi H (First Author), Main researcher /Discussion Writer (50%); Zand Moghadam MR (Second Author), Methodologist/Discussion Writer (25%); Kamyabi S (Third Author), Assistant Researcher (25%)
Funding: All expenses were covered by Hamzeh Behroozi, and no organization served as a financial sponsor
Keywords:
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