Modeling Fire Ignition Probability in the Wildland-Urban Interface: Focusing on Power Infrastructure and Surrounding Environment

Wildfire caused by faults in power distribution infrastructure is a typical threat in the wildland-urban interface (WUI) area. With advanced sensing technologies and deep learning approaches, several machine vision-based surveillance methods have been developed to determine the contact point of power distribution infrastructure and vegetation. However, there is still room for improvement, especially in terms of dealing with uncertainty associated with the risk of failures by a sudden gust of wind that does not reflect historical patterns. In this regard, this research aims to assess the impact of wind on wildfire ignition probability. This research evaluates the likelihood of fire ignition that could be induced by tree encroachment into the minimum vegetation clearance distance zone surrounding power lines. The result would be expressed as a set of coefficients that describe the effect of wind on reducing the ambient air temperature and arc duration and enlarging the interface area. As a proof of concept, the wildfire risk variance following wind speed is analyzed based on the point cloud data of power grids. Possible applications of this research include identifying the potential fire ignition sources near power lines, and ultimately, this research can be used to advance probabilistic analysis for supporting decision-making to explore how vulnerable environment interacts with the associated urban infrastructure.