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Volume 15, Issue 1 (Spring 2025)
Disaster Prev. Manag. Know. 2025, 15(1): 30-49 |
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Ehrambaf Shooshtar A, Samouei P, Messi Bidgoli M. Evaluation and Prioritization of Nodes in Urban Critical Infrastructures for Increasing Resilience Against Disasters Using the DEMATEL Approach. Disaster Prev. Manag. Know. 2025; 15 (1) :30-49
URL: http://dpmk.ir/article-1-709-en.html
URL: http://dpmk.ir/article-1-709-en.html
1- Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.
2- Department of Industrial Engineering, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, Iran.
2- Department of Industrial Engineering, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, Iran.
Keywords: Interdependent urban infrastructures, Crisis management, Network strengthening, Resilience, Prioritization, Critical nodes
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Almoghathawi, Y., Selim, S., & Barker, K. (2023). Community structure recovery optimization for partial disruption, functionality, and restoration in interdependent networks. Reliability Engineering & System Safety, 229, 108853. [DOI: 10.1016/j.ress.2022.108853]
Balakrishnan, S., & Zhang, Z. (2020). Criticality and susceptibility indexes for resilience-based ranking and prioritization of components in interdependent infrastructure networks. Journal of Management in Engineering, 36(4), 04020022. [Link]
Barnett, K. (2020). Decentralized resource allocation for interdependent infrastructure networks restoration: A Game Theory Approach [MA thesis]. Oklahoma: University of Oklahoma. [Link]
Baykasoğlu, A., & Gölcük, İ. (2017). Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Systems with Applications, 70, 37-51. [DOI: 10.1016/j.eswa.2016.11.001]
Chopra, S. S., & Khanna, V. (2015). Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience. Physica A: Statistical Mechanics and its Applications, 436, 865-877. [DOI:10.1016/j.physa.2015.05.091]
Fan, X., Zhang, X., Wang, X., & Yu, X. (2023). A deep reinforcement learning model for resilient road network recovery under earthquake or flooding hazards. Journal of Infrastructure Preservation and Resilience, 4(1), 8. [DOI: 10.1186/s43065-023-00072-x]
Huang, C. N., Liou, J. J., & Chuang, Y. C. (2014). A method for exploring the interdependencies and importance of critical infrastructures. Knowledge-Based Systems, 55, 66-74. [DOI: 10.1016/j.knosys.2013.10.010]
Kuttler, E., Barker, K., & Johansson, J. (2020). Network importance measures for multi-component disruptions. In 2020 Systems and Information Engineering Design Symposium (SIEDS) (pp. 1-6). IEEE. [Link]
Li, Q., Yu, H., Li, S., & Liu, S. (2024). Cascading failures in interdependent networks with reinforced crucial nodes and dependency groups. International Journal of Modern Physics C (IJMPC), 35(5), 1-27. [DOI:10.1142/S0129183124500554]
Oliva, G., Setola, R., & Barker, K. (2014). Fuzzy importance measures for ranking key interdependent sectors under uncertainty. IEEE Transactions on Reliability, 63(1), 42-57. [Link]
Ouyang, M. (2016). Critical location identification and vulnerability analysis of interdependent infrastructure systems under spatially localized attacks. Reliability Engineering & System Safety, 154, 106-116. [DOI: 10.1016/j.ress.2016.05.007]
Rezapour, S. A coupled reinforcement learning mechanism for concurrent restoration of interdependent critical infrastructures. [Link]
Shahverdi, B., Miller-Hooks, E., & Isaac, S. (2024). Decision support for prioritizing critical societal services in optimal post-disaster critical lifeline recovery. OR Spectrum, 1-37. [DOI:10.1007/s00291-024-00777-9]
Shen, S. (2013). Optimizing designs and operations of a single network or multiple interdependent infrastructures under stochastic arc disruption. Computers & Operations Research, 40(11), 2677-2688. [DOI: 10.1016/j.cor.2013.05.002]
Sütiçen, T. C., Batun, S., & Çelik, M. (2023). Integrated reinforcement and repair of interdependent infrastructure networks under disaster-related uncertainties. European Journal of Operational Research, 308(1), 369-384. [DOI: 10.1016/j.ejor.2022.10.043]
Sotoudeh-Anvari, A. (2023). Root Assessment Method (RAM): A novel multi-criteria decision making method and its applications in sustainability challenges. Journal of Cleaner Production, 423, 138695. [DOI: 10.1016/j.jclepro.2023.138695]
Ugwu, I. A., Salarieh, B., Salman, A. M., Petnga, L., & Williams, M. Y. (2022). Postdisaster recovery planning for interdependent infrastructure systems prioritizing the functionality of healthcare facilities. Journal of Infrastructure Systems, 28(4), 04022038. [DOI: 10.1061/(ASCE)IS.1943-555X.0000719]
Wang, S., Sun, J., Zhang, J., Dong, Q., Gu, X., & Chen, C. (2023). Attack-Defense game analysis of critical infrastructure network based on Cournot model with fixed operating nodes. International Journal of Critical Infrastructure Protection, 40, 100583. [DOI: 10.1016/j.ijcip.2022.100583]
Xu, M., Ouyang, M., Mao, Z., & Xu, X. (2019). Improving repair sequence scheduling methods for postdisaster critical infrastructure systems. Computer-Aided Civil and Infrastructure Engineering, 34(6), 506-522. [DOI: 10.1111/mice.12435]
Yuan, X., Hu, Y., Stanley, H. E., & Havlin, S. (2017). Eradicating catastrophic collapse in interdependent networks via reinforced nodes. Proceedings of the National Academy of Sciences of the United States of America, 114(13), 3311–3315. [DOI: 10.1073/pnas.1621369114] [PMID]
Yazdani, M., Chatterjee, P., Zavadskas, E. K., & Zolfani, S. H. (2017). Integrated QFD-MCDM framework for green supplier selection. Journal of Cleaner Production, 142, 3728-3740. [DOI:10.1016/j.jclepro.2016.10.095]
Zhang, W., Han, Q., Dong, H., Wen, J., & Xu, C. (2024). Resilience-based post-earthquake restoration scheduling for urban interdependent transportation-electric power network. Structure and Infrastructure Engineering, 1-18. [DOI: 10.1080/15732479.2024.2401374]
Zhao, C., Li, N., & Fang, D. (2018). Criticality assessment of urban interdependent lifeline systems using a biased PageRank algorithm and a multilayer weighted directed network model. International Journal of Critical Infrastructure Protection, 22, 100-112. [DOI:10.1016/j.ijcip.2018.06.002]
Zhang, W. J., Liu, X., Chai, C. L., Deters, R., Liu, D., & Dyachuk, D., et al. (2008). Social network analysis of the vulnerabilities of interdependent critical infrastructures. International Journal of Critical Infrastructures, 4(3), 256-273. [Link]
Zhang, X., & Su, J. (2019). A combined fuzzy DEMATEL and TOPSIS approach for estimating participants in knowledge-intensive crowdsourcing. Computers & Industrial Engineering, 137, 106085. [DOI: 10.1016/j.cie.2019.106085]
Full-Text: (95 Views)
Introduction
Natural or human-made disasters lead to large-scale disruptions in critical infrastructures such as electricity networks, water networks, and telecommunication. Recovering these infrastructures is critical after a disaster so that relief efforts and activities can be facilitated. The operations of these infrastructures often depend on receiving services from one another, resulting in an interdependent network structure. A disruption in one of these infrastructures can has a cascade effect on others, disrupting the entire system. In this research, considering the existing limitations, the primary objective is to present an efficient, data-driven approach for prioritizing the components of critical infrastructures to implement reinforcement measures and enhance the resilience of the entire system. This study aims to propose a comprehensive method for identifying shortcomings and prioritizing reinforcement actions by modeling and analyzing complex networks. By focusing on critical nodes, this research provides a framework to support more efficient resource allocation during a disaster.
Methods
Based on data from a network of water, electricity, and wastewater infrastructure obtained from a previous case study (Sioux Falls in South Dakota), this study applies the DEMATEL and the Root Assessment Method (RAM). The DEMATEL method is one of the multi-criteria decision-making (MCDM) approaches used for identifying and weighting criteria in complex problems with multiple qualitative and quantitative criteria. The RAM is simple to comprehend, easy to apply, and comparable to other established MCDM approaches. RAM is inspired by the limitations of existing MCDM methods in achieving dependable outcomes in decision-making situations. Moreover, it emphasizes that decision-makers understand the entire decision-making process to prevent potential computational errors. The integration of RAM and DEMATEL ensures a comprehensive evaluation, capable of handling the intricacies of interdependent infrastructures and accounting for both qualitative and quantitative factors in decision-making. Using these methods, we can identify the more critical points of these networks to reinforce them.
In this study, the network had 21 nodes and the main criteria included node capacity, supply node, transmission node, connection with other networks (such as communication networks), and repair cost. There were also three sub-criteria: electricity, water, and wastewater networks. We obtained the weight of the criteria using the DEMATEL method. Then, using the RAM, we prioritized the critical nodes to reinforce interdependent infrastructure networks in the pre-disaster phase.
Results
By using the DEMATEL method in determining the relative weight of each criterion, the results showed that the node capacity had the highest importance in decision-making؛ as a result, increasing the capacity of nodes can have a significant impact on improving network performance. Among the sub-criteria of each main criterion, in most cases, electricity had the highest local weight, which confirms the importance of the electricity infrastructure in this field. By using the RAM, the results showed that nodes 5, 6, 12, 13, and 15 had the highest importance and were identified as critical points that require more attention and reinforcement. These nodes, if they fail, can have a widespread impact on the entire network. Therefore, their accurate prioritization for resource allocation and the implementation of improvement programs is essential. Parameters such as node capacity, repair time, and reinforcement cost also play an important role in determining the importance of nodes. Nodes with high capacity and short repair time are less important because disruptions can be quickly compensated. Moreover, the analysis highlights the need for dynamic resilience strategies that address both short-term recovery and long-term infrastructure stability. The findings suggest that these strategies must account for varying levels of interdependency, ensuring that reinforcement efforts are directed towards nodes with the greatest potential for mitigating cascading failures.
Conclusion
The findings show that the node capacity criterion, as the most influential factor, plays a key role in managing and improving critical infrastructures (water, wastewater, and electricity networks). Reinforcement of the identified nodes with the highest importance can significantly increase the resilience and performance of critical infrastructures. These nodes are usually transfer nodes or main facilities whose disruption can have a widespread impact on the entire network. Therefore, it is recommended that limited resources for reinforcing infrastructures be focused on the identified critical nodes. Also, special attention should be paid to the interdependencies between critical infrastructures, and the necessary measures should be taken to reduce these dependencies. The integration of the DEMATEL and RAM provides a robust framework for reinforcing critical infrastructure, ultimately enhancing the system’s resilience to future disasters. This research provides a significant contribution to the literature regarding disaster preparedness and infrastructure management. The results can help decision-makers in Iran in increasing the resilience of critical infrastructures by optimally allocating resources and making the community more resilient to disasters.
Ethical Considerations
Compliance with ethical guidelines
There is not any ethical guidelines related to this paper.
Funding
This paper is originated from Ms Azar Ehrambaaf PhD disseration. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Authors' contributions
Conceptualization and Methodology: Azar Ehrambaf Shooshtar, Parvaneh Samouei, Masumeh Messi Bidgoli; Investigation, Writing–Review & Editing and Supervision: Parvaneh Samouei and Masumeh Messi Bidgoli; Writing–Original Draft: Azar Ehrambaf Shooshtar.
Conflicts of interest
The authors declared no conflict of interest.
Natural or human-made disasters lead to large-scale disruptions in critical infrastructures such as electricity networks, water networks, and telecommunication. Recovering these infrastructures is critical after a disaster so that relief efforts and activities can be facilitated. The operations of these infrastructures often depend on receiving services from one another, resulting in an interdependent network structure. A disruption in one of these infrastructures can has a cascade effect on others, disrupting the entire system. In this research, considering the existing limitations, the primary objective is to present an efficient, data-driven approach for prioritizing the components of critical infrastructures to implement reinforcement measures and enhance the resilience of the entire system. This study aims to propose a comprehensive method for identifying shortcomings and prioritizing reinforcement actions by modeling and analyzing complex networks. By focusing on critical nodes, this research provides a framework to support more efficient resource allocation during a disaster.
Methods
Based on data from a network of water, electricity, and wastewater infrastructure obtained from a previous case study (Sioux Falls in South Dakota), this study applies the DEMATEL and the Root Assessment Method (RAM). The DEMATEL method is one of the multi-criteria decision-making (MCDM) approaches used for identifying and weighting criteria in complex problems with multiple qualitative and quantitative criteria. The RAM is simple to comprehend, easy to apply, and comparable to other established MCDM approaches. RAM is inspired by the limitations of existing MCDM methods in achieving dependable outcomes in decision-making situations. Moreover, it emphasizes that decision-makers understand the entire decision-making process to prevent potential computational errors. The integration of RAM and DEMATEL ensures a comprehensive evaluation, capable of handling the intricacies of interdependent infrastructures and accounting for both qualitative and quantitative factors in decision-making. Using these methods, we can identify the more critical points of these networks to reinforce them.
In this study, the network had 21 nodes and the main criteria included node capacity, supply node, transmission node, connection with other networks (such as communication networks), and repair cost. There were also three sub-criteria: electricity, water, and wastewater networks. We obtained the weight of the criteria using the DEMATEL method. Then, using the RAM, we prioritized the critical nodes to reinforce interdependent infrastructure networks in the pre-disaster phase.
Results
By using the DEMATEL method in determining the relative weight of each criterion, the results showed that the node capacity had the highest importance in decision-making؛ as a result, increasing the capacity of nodes can have a significant impact on improving network performance. Among the sub-criteria of each main criterion, in most cases, electricity had the highest local weight, which confirms the importance of the electricity infrastructure in this field. By using the RAM, the results showed that nodes 5, 6, 12, 13, and 15 had the highest importance and were identified as critical points that require more attention and reinforcement. These nodes, if they fail, can have a widespread impact on the entire network. Therefore, their accurate prioritization for resource allocation and the implementation of improvement programs is essential. Parameters such as node capacity, repair time, and reinforcement cost also play an important role in determining the importance of nodes. Nodes with high capacity and short repair time are less important because disruptions can be quickly compensated. Moreover, the analysis highlights the need for dynamic resilience strategies that address both short-term recovery and long-term infrastructure stability. The findings suggest that these strategies must account for varying levels of interdependency, ensuring that reinforcement efforts are directed towards nodes with the greatest potential for mitigating cascading failures.
Conclusion
The findings show that the node capacity criterion, as the most influential factor, plays a key role in managing and improving critical infrastructures (water, wastewater, and electricity networks). Reinforcement of the identified nodes with the highest importance can significantly increase the resilience and performance of critical infrastructures. These nodes are usually transfer nodes or main facilities whose disruption can have a widespread impact on the entire network. Therefore, it is recommended that limited resources for reinforcing infrastructures be focused on the identified critical nodes. Also, special attention should be paid to the interdependencies between critical infrastructures, and the necessary measures should be taken to reduce these dependencies. The integration of the DEMATEL and RAM provides a robust framework for reinforcing critical infrastructure, ultimately enhancing the system’s resilience to future disasters. This research provides a significant contribution to the literature regarding disaster preparedness and infrastructure management. The results can help decision-makers in Iran in increasing the resilience of critical infrastructures by optimally allocating resources and making the community more resilient to disasters.
Ethical Considerations
Compliance with ethical guidelines
There is not any ethical guidelines related to this paper.
Funding
This paper is originated from Ms Azar Ehrambaaf PhD disseration. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Authors' contributions
Conceptualization and Methodology: Azar Ehrambaf Shooshtar, Parvaneh Samouei, Masumeh Messi Bidgoli; Investigation, Writing–Review & Editing and Supervision: Parvaneh Samouei and Masumeh Messi Bidgoli; Writing–Original Draft: Azar Ehrambaf Shooshtar.
Conflicts of interest
The authors declared no conflict of interest.
References
Almoghathawi, Y., González, A. D., & Barker, K. (2021). Exploring recovery strategies for optimal interdependent infrastructure network resilience. Networks and Spatial Economics, 21, 229-260. [DOI:10.1007/s11067-020-09515-4]Almoghathawi, Y., Selim, S., & Barker, K. (2023). Community structure recovery optimization for partial disruption, functionality, and restoration in interdependent networks. Reliability Engineering & System Safety, 229, 108853. [DOI: 10.1016/j.ress.2022.108853]
Balakrishnan, S., & Zhang, Z. (2020). Criticality and susceptibility indexes for resilience-based ranking and prioritization of components in interdependent infrastructure networks. Journal of Management in Engineering, 36(4), 04020022. [Link]
Barnett, K. (2020). Decentralized resource allocation for interdependent infrastructure networks restoration: A Game Theory Approach [MA thesis]. Oklahoma: University of Oklahoma. [Link]
Baykasoğlu, A., & Gölcük, İ. (2017). Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Systems with Applications, 70, 37-51. [DOI: 10.1016/j.eswa.2016.11.001]
Chopra, S. S., & Khanna, V. (2015). Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience. Physica A: Statistical Mechanics and its Applications, 436, 865-877. [DOI:10.1016/j.physa.2015.05.091]
Fan, X., Zhang, X., Wang, X., & Yu, X. (2023). A deep reinforcement learning model for resilient road network recovery under earthquake or flooding hazards. Journal of Infrastructure Preservation and Resilience, 4(1), 8. [DOI: 10.1186/s43065-023-00072-x]
Huang, C. N., Liou, J. J., & Chuang, Y. C. (2014). A method for exploring the interdependencies and importance of critical infrastructures. Knowledge-Based Systems, 55, 66-74. [DOI: 10.1016/j.knosys.2013.10.010]
Kuttler, E., Barker, K., & Johansson, J. (2020). Network importance measures for multi-component disruptions. In 2020 Systems and Information Engineering Design Symposium (SIEDS) (pp. 1-6). IEEE. [Link]
Li, Q., Yu, H., Li, S., & Liu, S. (2024). Cascading failures in interdependent networks with reinforced crucial nodes and dependency groups. International Journal of Modern Physics C (IJMPC), 35(5), 1-27. [DOI:10.1142/S0129183124500554]
Oliva, G., Setola, R., & Barker, K. (2014). Fuzzy importance measures for ranking key interdependent sectors under uncertainty. IEEE Transactions on Reliability, 63(1), 42-57. [Link]
Ouyang, M. (2016). Critical location identification and vulnerability analysis of interdependent infrastructure systems under spatially localized attacks. Reliability Engineering & System Safety, 154, 106-116. [DOI: 10.1016/j.ress.2016.05.007]
Rezapour, S. A coupled reinforcement learning mechanism for concurrent restoration of interdependent critical infrastructures. [Link]
Shahverdi, B., Miller-Hooks, E., & Isaac, S. (2024). Decision support for prioritizing critical societal services in optimal post-disaster critical lifeline recovery. OR Spectrum, 1-37. [DOI:10.1007/s00291-024-00777-9]
Shen, S. (2013). Optimizing designs and operations of a single network or multiple interdependent infrastructures under stochastic arc disruption. Computers & Operations Research, 40(11), 2677-2688. [DOI: 10.1016/j.cor.2013.05.002]
Sütiçen, T. C., Batun, S., & Çelik, M. (2023). Integrated reinforcement and repair of interdependent infrastructure networks under disaster-related uncertainties. European Journal of Operational Research, 308(1), 369-384. [DOI: 10.1016/j.ejor.2022.10.043]
Sotoudeh-Anvari, A. (2023). Root Assessment Method (RAM): A novel multi-criteria decision making method and its applications in sustainability challenges. Journal of Cleaner Production, 423, 138695. [DOI: 10.1016/j.jclepro.2023.138695]
Ugwu, I. A., Salarieh, B., Salman, A. M., Petnga, L., & Williams, M. Y. (2022). Postdisaster recovery planning for interdependent infrastructure systems prioritizing the functionality of healthcare facilities. Journal of Infrastructure Systems, 28(4), 04022038. [DOI: 10.1061/(ASCE)IS.1943-555X.0000719]
Wang, S., Sun, J., Zhang, J., Dong, Q., Gu, X., & Chen, C. (2023). Attack-Defense game analysis of critical infrastructure network based on Cournot model with fixed operating nodes. International Journal of Critical Infrastructure Protection, 40, 100583. [DOI: 10.1016/j.ijcip.2022.100583]
Xu, M., Ouyang, M., Mao, Z., & Xu, X. (2019). Improving repair sequence scheduling methods for postdisaster critical infrastructure systems. Computer-Aided Civil and Infrastructure Engineering, 34(6), 506-522. [DOI: 10.1111/mice.12435]
Yuan, X., Hu, Y., Stanley, H. E., & Havlin, S. (2017). Eradicating catastrophic collapse in interdependent networks via reinforced nodes. Proceedings of the National Academy of Sciences of the United States of America, 114(13), 3311–3315. [DOI: 10.1073/pnas.1621369114] [PMID]
Yazdani, M., Chatterjee, P., Zavadskas, E. K., & Zolfani, S. H. (2017). Integrated QFD-MCDM framework for green supplier selection. Journal of Cleaner Production, 142, 3728-3740. [DOI:10.1016/j.jclepro.2016.10.095]
Zhang, W., Han, Q., Dong, H., Wen, J., & Xu, C. (2024). Resilience-based post-earthquake restoration scheduling for urban interdependent transportation-electric power network. Structure and Infrastructure Engineering, 1-18. [DOI: 10.1080/15732479.2024.2401374]
Zhao, C., Li, N., & Fang, D. (2018). Criticality assessment of urban interdependent lifeline systems using a biased PageRank algorithm and a multilayer weighted directed network model. International Journal of Critical Infrastructure Protection, 22, 100-112. [DOI:10.1016/j.ijcip.2018.06.002]
Zhang, W. J., Liu, X., Chai, C. L., Deters, R., Liu, D., & Dyachuk, D., et al. (2008). Social network analysis of the vulnerabilities of interdependent critical infrastructures. International Journal of Critical Infrastructures, 4(3), 256-273. [Link]
Zhang, X., & Su, J. (2019). A combined fuzzy DEMATEL and TOPSIS approach for estimating participants in knowledge-intensive crowdsourcing. Computers & Industrial Engineering, 137, 106085. [DOI: 10.1016/j.cie.2019.106085]
Type of Study: Research |
Subject:
Special
Received: 2024/08/14 | Accepted: 2024/10/14 | ePublished: 2025/03/30
Received: 2024/08/14 | Accepted: 2024/10/14 | ePublished: 2025/03/30
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