Mapping Cascading Critical Infrastructure Service Disruptions in Disaster Scenarios Using DISruptionMap

Khái niệm cốt lõi

DISruptionMap is a novel method combining GIS and Bayesian Networks to model and visualize cascading service disruptions in critical infrastructure during large-scale disasters, aiding disaster preparedness and response.

Tóm tắt

• Bibliographic Information: Schneider, M., Halekotte, L., Mentges, A., & Fiedrich, F. (2024). Dependent Infrastructure Service Disruption Mapping (DISruptionMap): A Method to Assess Cascading Service Disruptions in Disaster Scenarios. arXiv preprint arXiv:2410.05286v1.
• Research Objective: This paper introduces DISruptionMap, a novel method for assessing both direct and indirect (cascading) disruptions to critical infrastructure services during large-scale disaster scenarios.
• Methodology: DISruptionMap combines GIS-based spatial models to assess direct service disruptions with a Bayesian Network (BN) based service dependency model to assess indirect disruptions. The spatial models utilize hazard maps, critical infrastructure location data, and fragility curves to determine the probability of component failures. The BN model, constructed using expert knowledge, represents service dependencies and quantifies the probability of cascading failures. The method is demonstrated through a case study of a flood scenario in Cologne, Germany, focusing on the impact on hospital emergency care services.
• Key Findings: The case study demonstrates that DISruptionMap can effectively identify critical infrastructures and services vulnerable to cascading disruptions. The method highlights the importance of considering both direct and indirect impacts for comprehensive disaster risk assessment. The visualization of results through an interactive dashboard allows for a clear understanding of the spatial distribution of service disruptions, aiding decision-making in disaster preparedness and response.
• Main Conclusions: DISruptionMap provides a practical and versatile approach for assessing cascading critical infrastructure service disruptions. The method's reliance on readily available data and expert knowledge makes it suitable for local disaster management authorities. The integration of spatial and dependency models allows for a comprehensive understanding of the complex interdependencies within critical infrastructure systems.
• Significance: This research contributes to the field of disaster risk management by providing a practical tool for assessing cascading effects on critical infrastructure. The method's focus on service dependencies offers a valuable perspective for understanding the broader societal impacts of disaster events.
• Limitations and Future Research: The current version of DISruptionMap relies on simplifying assumptions, such as binary service states and neglecting temporal dynamics. Future research could explore incorporating more granular service states, temporal factors, and interdependencies between critical infrastructures to enhance the model's realism and accuracy.

Thống kê

Out of 27 hospitals examined in Cologne, 8 are located within the flood zone with depths ranging from 1 cm to 2.38 m.
6 hospitals are inaccessible due to direct road flooding or network disconnection.
5 hospitals face uncertain power supply.
1 hospital shows definite power failure due to grid and backup generator failure.
7 hospitals show complete emergency care service failure due to inaccessibility, power failure, high flood levels, or a combination of these factors.
2 hospitals show uncertainty in emergency care service status.

Trích dẫn

"DISruptionMap enables a spatial assessment of direct as well as indirect CI service disruptions and allows the effective integration of expert knowledge while requiring a minimum amount of information."
"The fault tree-based approach reduces the amount of input information required for the CPTs, by using logical operators (i.e. gates) to describe the conditions for service availability. This significantly reduces the workload for the consulted experts."

Thông tin chi tiết chính được chắt lọc từ

Dependent Infrastructure Service Disruption Mapping (DISruptionMap): A Method to Assess Cascading Service Disruptions in Disaster Scenarios

by Moritz Schne... lúc arxiv.org 10-10-2024

https://arxiv.org/pdf/2410.05286.pdf

Yêu cầu sâu hơn

How can DISruptionMap be integrated with early warning systems to provide real-time risk assessment and inform proactive disaster response measures?

Integrating DISruptionMap with early warning systems holds significant potential for enhancing real-time risk assessment and enabling proactive disaster response. Here's a breakdown of how this integration can be achieved and its benefits:
Integration Process:

Real-time Hazard Data Input: Early warning systems, often utilizing sensor networks and predictive models, can provide real-time data on the evolving hazard situation. This data, such as flood stage levels, earthquake magnitudes, or hurricane track forecasts, can be directly fed into DISruptionMap.

Dynamic Updating of Spatial Models: DISruptionMap's GIS-based spatial models, which assess direct disruptions to critical infrastructure components (e.g., roads, power lines), can be dynamically updated using the real-time hazard data. For instance, as floodwaters rise, the model can continuously reassess which road segments become impassable.

Probabilistic Service Disruption Assessment: The Bayesian network component of DISruptionMap can then use the updated spatial model outputs to generate probabilistic assessments of service disruptions. This means that instead of just showing areas as "affected" or "unaffected," the system can provide probabilities of service availability for different locations and times.

Visualization and Decision Support: The results, visualized on the interactive dashboard, would provide emergency managers with a dynamic understanding of the cascading impacts of the unfolding disaster. This real-time insight enables informed decision-making regarding resource allocation, evacuation orders, and preemptive infrastructure protection measures.

Benefits of Integration:

Proactive Response: By anticipating cascading failures, authorities can take proactive steps to mitigate impacts, such as deploying backup generators to hospitals at risk of power loss.
Targeted Interventions: Real-time risk assessment allows for prioritizing response efforts and resources to areas facing the most severe potential disruptions.
Improved Communication: The dynamic visualization of evolving risks facilitates clearer communication of the situation to both responders and the public.
Challenges:

Data Accuracy and Latency: The effectiveness relies on the accuracy and timeliness of real-time hazard data. Delays or inaccuracies can impact the reliability of risk assessments.
Computational Demands: Processing real-time data and continuously updating the model can be computationally intensive, requiring robust IT infrastructure.

Could the reliance on expert knowledge introduce biases into the model, and how can these biases be mitigated or accounted for in the analysis?

The reliance on expert knowledge in DISruptionMap, particularly in defining service dependencies and populating conditional probability tables (CPTs), can indeed introduce biases. Here's a closer look at the potential biases and mitigation strategies:
Potential Biases:

Availability Bias: Experts might overemphasize dependencies based on recent or memorable events, neglecting less frequent but potentially impactful scenarios.
Anchoring Bias: Initial estimates provided by experts can anchor subsequent judgments, even when new information emerges.
Confirmation Bias: Experts might unconsciously favor information that confirms their existing beliefs about infrastructure vulnerabilities.
Mitigation Strategies:

Structured Elicitation Techniques: Employing structured protocols for eliciting expert judgments, such as Delphi methods or Nominal Group Technique, can minimize biases and promote a more systematic approach.
Multiple Expert Perspectives: Engaging a diverse group of experts with varying backgrounds and areas of expertise can help challenge assumptions and reduce individual biases.
Sensitivity Analysis: Conducting sensitivity analyses can reveal how variations in expert judgments impact the model's outputs, highlighting areas of uncertainty.
Data-Driven Validation: Wherever possible, comparing model predictions with historical data or results from simulations can help identify and correct for biases.
Transparency and Documentation: Clearly documenting the expert elicitation process, assumptions made, and data sources used enhances transparency and allows for scrutiny and future refinement.

If a city were to prioritize resilience upgrades for one critical infrastructure sector based on DISruptionMap findings, what ethical considerations should be factored into that decision-making process?

Prioritizing resilience upgrades for one critical infrastructure sector over others based on DISruptionMap findings raises significant ethical considerations. Here are key factors that demand careful attention:

Equity and Distributive Justice: Resilience upgrades should not disproportionately benefit certain populations or neighborhoods while leaving others vulnerable. The decision-making process must consider the potential for exacerbating existing social inequalities.

Transparency and Public Engagement: Openly communicating the rationale behind prioritizing one sector over others, including the data and model used, is crucial. Public consultations and engagement can provide valuable insights and ensure that diverse perspectives are considered.

Vulnerable Populations: Special attention must be given to the needs of vulnerable populations, such as low-income communities, the elderly, or individuals with disabilities, who might be disproportionately impacted by infrastructure failures.

Long-Term Sustainability: Resilience investments should align with long-term sustainability goals, considering environmental impacts and the potential for maladaptation, where short-term solutions exacerbate vulnerabilities in the future.

Accountability and Review: Establishing mechanisms for ongoing monitoring, evaluation, and accountability ensures that resilience investments are effective and equitable. Regular reviews of the prioritization process can incorporate lessons learned and adapt to changing circumstances.
Ethical Decision-Making Framework:

Identify Stakeholders: Clearly define all stakeholders potentially affected by the decision, including residents, businesses, and vulnerable groups.
Assess Impacts: Evaluate the potential benefits and burdens of prioritizing each sector, considering both short-term and long-term consequences.
Promote Equity: Analyze the distributional impacts of the decision, ensuring that it does not disproportionately benefit or harm specific groups.
Facilitate Transparency: Communicate the decision-making process, data, and rationale clearly and accessibly to all stakeholders.
Ensure Accountability: Establish mechanisms for ongoing monitoring, evaluation, and public feedback to ensure ethical and equitable outcomes.