Integrated graph measures reveal survival likelihood for buildings in wildfre events
Wildfre events have resulted in unprecedented social and economic losses worldwide in the last few years. Most studies on reducing wildfre risk to communities focused on modeling wildfre behavior in the wildland to aid in developing fuel reduction and fre suppression strategies. However, minimizing losses in communities and managing risk requires a holistic approach to understanding wildfre behavior that fully integrates the wildland’s characteristics and the built environment’s features. This complete integration is particularly critical for intermixed communities where the wildland and the built environment coalesce. Community-level wildfre behavior that captures the interaction between the wildland and the built environment, which is necessary for predicting structural damage, has not received sufcient attention. Predicting damage to the built environment is essential in understanding and developing fre mitigation strategies to make communities more resilient to wildfre events. In this study, we use integrated concepts from graph theory to establish a relative vulnerability metric capable of quantifying the survival likelihood of individual buildings within a wildfre-afected region. We test the framework by emulating the damage observed in the historic 2018 Camp Fire and the 2020 Glass Fire. We propose two formulations based on graph centralities to evaluate the vulnerability of buildings relative to each other. We then utilize the relative vulnerability values to determine the damage state of individual buildings. Based on a one-to-one comparison of the calculated and observed damages, the maximum predicted building survival accuracy for the two formulations ranged from 58 − 64% for the historical wildfres tested. From the results, we observe that the modifed random walk formulation can better identify nodes that lie at the extremes on the vulnerability scale. In contrast, the modifed degree formulation provides better predictions for nodes with mid-range vulnerability values.
Journal or Serie
Scientific Reports, 2022, vol. 12, art. 15954