A UBC Okanagan (UBCO) doctoral student has utilized an artificial intelligence (AI) tool to study fire behaviour.
Laden Tazik, the lead author of a new study in Fire, used the “Segment Anything Model”, a state of the art AI tool to capture fire behaviour and extract fire perimeters from experimental burn videos frame-by-frame to study fire spread dynamics.
In practical terms, her research shows that fire is shaped by randomness, far more than today’s deterministic models capture. Her work could help reshape how fire behaviour is modelled and forecasted in an era of worsening wildfire seasons.
“By capturing the randomness in how fires spread, we can build models that better reflect reality and help improve decision-making during active fire event” said Tazik.
Her analysis confirmed that fires race uphill, but when she compared her measurements with the values used in Canada’s official Fire Behaviour Prediction system, the numbers didn’t always line up. She realized real fire often moved faster, and that slope wasn’t always consistent, prompting her to test her method on ponderosa pine and douglas fir fuels often used in fire research. Even under nearly identical conditions, the flames didn’t behave the same way twice.
“These results show that we need to pair every spread estimate with a measure of uncertainty,” Tazik explains. “Simply multiplying by a slope factor isn’t enough. Fire is dynamic, and our models should acknowledge that.”
Tazik added the next step is to expand the approach to more fuel types and fire conditions to build models that better capture wildfire dynamics while embracing the inherent uncertainty of fire.
“Fires don’t behave perfectly, our tools shouldn’t pretend they do,” added Tazik.
Her work is supervised by Dr. W. John Braun with other contributions from Dr. John R.J. Thompson, and other partners who provided the experimental and field video datasets.
“Tazik proposed innovative ways to tackle this difficult modelling problem,” Braun said. “Her work shows how high-resolution perimeter data and advanced modelling can help us understand the real variability in fire behaviour. That’s essential if we want to move toward more probabilistic, data-driven prediction systems.”