Scientists at the University of Alberta hope to harness the powers of to protect Alberta communities from the ravages of wildfires.
A computer model inspired by the human brain, programmed by forest science researchers at the University of Alberta and the University of Oklahoma, is designed to predict extreme fire weather in northern Alberta. Described as a “self-organizing map,” or an SOM, the program relies on raw meteorological data to generate predictions. Over time, without direction or outside intervention, the program “learns” from this raw data and makes predictions in real time, said Mike Flannigan, co-author of the study and professor at the University of Alberta’s department of renewable resources.
Training a fire-fighting algorithm
Similar to the human mind, the program can be “trained” to find patterns and draw complex conclusions about future forecasts. The program could help bolster Alberta’s early warning system, allowing front-line staff to better deploy resources and brace for possible evacuations. “Most of the impacts from wildland fire happen during a short period of extreme fire weather that is hot, dry, and windy,” said Flannigan. “Having an early warning system can give you intelligence to better prepare for the coming situation.” The Fort McMurray wildfire was named the costliest insured natural disaster in Canadian history. (Sylvain Bascaron/CBC Edmonton ) Unlike other statistical modelling, the U of A program can provide “more robust” predictions because it accounts for complex factors related to wildfires, such as growth rate and intensity, said Flannigan.
Application to other natural catastrophes
This kind of modelling has been used to predict other extreme weather events such as monsoons, but this is the first time it has been applied to wildfires. “We’re using self-organized maps but it uses a neural network and it is supposed to mimic how we have neurons in our brains and how they pass information along and how they learn,” Flannigan said. “These methods do exactly the same sort of thing.” […]