Researchers have invented a model that predicts which building will survive wildfire which could help in developing fire mitigation strategies and steps for recovery
A growing number of population shifting to wildlands increases the risks of a wildfire burning their habitat. Few homes are like super-spreaders, which are more at risk of fire and more likely to transmit fire to other homes. Hence, it is necessary to predict damage to the built environment for developing essential fire mitigation strategies and steps for recovery, as wildfires deteriorate human life to a great extent. Therefore, CSU engineers have implemented a model that can predict how a wildfire will impact a community down to which buildings will burn. This allows the community to develop strategic approaches for reducing damage from wildfires and speedy recovery.
“We’re able to predict the most probable path the fire will take and how vulnerable each home is relative to the neighboring homes,” said Mahmoud, a Civil and Environmental Engineering professor. “We put a spin on the original model that allows us now to determine the level of damage in each building, whether the building will burn or survive.”
They developed their model by employing graph theory, which is used to analyze networks. Mahmoud and Chulahwat’s model was the first to predict how a fire would progress through a community. For experimentation they used data from Technosylva, a wildfire science, and technology company, Mahmoud and Chulahwat tested their model on the 2018 Camp Fire and 2020 Glass Fire in California. The model predicted which buildings burned and which survived with 58-64% accuracy. The results published in Scientific Reports predicted which buildings burned with 86% accuracy for the Camp Fire by adjusting how the model weighs certain factors that contribute to damage.
Mahmoud says a holistic approach is needed to understand wildfire behavior and bolster resilience. Models that incorporate a community’s wildland and built environment features will give decision-makers the information needed to mitigate vulnerable areas.
Click here for the Published Research Paper