A dynamic Bayesian network approach to characterize multi-hazard risks and resilience in interconnected critical infrastructures
Bibliographic record
Abstract
A new paradigm for risk assessment has emerged, recognizing the escalating frequency and severity of disasters associated with natural hazards. Conventional risk assessments often fail to capture the dynamic and interconnected nature of disruptions within infrastructure systems during failure scenarios. This study introduces a Dynamic Bayesian Network (DBN) framework, designed to assess risk in interconnected infrastructure systems under complex hazard scenarios. The framework addresses the limitations of static models by dynamically capturing the progression of disruptions during failure and the restoration process during recovery. Using a case study in Saint Lucia, a Caribbean Island susceptible to natural hazards, this study examines the complex network of critical infrastructure. The DBN framework explores various failure scenarios, highlighting the cascading effects across infrastructure sectors, and captures the probabilistic hazard conditions and functional dynamics during disruption and restoration processes. Results from the case study illuminate the heightened vulnerability of the international airport and tourism sectors, emphasizing the interdependencies and propagation of failures within the infrastructure system. By investigating failure scenarios, the DBN approach characterizes the complex interactions between infrastructure systems, providing valuable insights into how multi-hazard events affect interconnected networks. These findings underscore the critical need for dynamic, real-time risk assessments that consider both short-term disruptions and long-term recovery processes. The study highlights the urgency of embracing dynamic risk assessment methodologies and offers a foundation for developing adaptive, multi-hazard risk assessment strategies to enhance the resilience of critical infrastructure networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".