Understanding Wildfire Induced Risk on Interconnected Infrastructure Systems Using a Bow-Tie Model with Bayesian Network and Self Organizing Maps

With increased global warming and urbanization, the risk of wildfires impacting the critical infrastructure systems like electricity distribution and transmission, telecommunication, and buildings is increasing in several parts of the world. With such critical infrastructure systems becoming increasingly interdependent, failure of one infrastructure may easily cascade to other dependent networks causing a widespread national scale failure. To analyze the risk of such infrastructure-failures, most of the studies have used either a fault tree or event tree analysis which often underestimates the risk leading to sub-optimal decision making. To address this gap, in this paper we propose a holistic infrastructure risk assessment framework that can integrate both the fault and event tree methods into a single bow-tie model to capture the wildfire-induced risk on multiple interdependent infrastructure systems. The proposed framework can capture multiple dimensions (i.e., physical, logical and geographical) of infrastructure interdependencies—including intra-infrastructure, and inter-infrastructure dependencies—onto a pixel-based risk map. We proposed a conceptual framework to calculate risk of wildfires on electricity system considering a simplified fault tree model of the electricity distribution network. Furthermore, using self-organizing maps we create quantized dynamic risk maps for efficient risk zoning and risk communication to the respective stakeholders. Our proposed framework will help the federal and state governments as well as the utilities to make risk-informed decisions related to resource allocations, and planning for wildfire risk mitigation.