How artificial intelligence could help predict major forest fires

Source(s)
Royal Canadian Geographical Society

By Alexandra Pope

Last spring, Canadians watched, horrified, as residents of Fort McMurray, Alta. were forced to flee their community as a nearby forest fire exploded out of control. Now, as neighbouring British Columbia continues to grapple with its worst wildfire season in recorded history, forest science researchers at the Universities of Alberta and Oklahoma say artificial intelligence may be key to predicting where the next major fire will start.

A paper published last month in the Canadian Journal of Forest Research describes the researchers’ efforts to train a computational model to predict extreme “fire weather” — the combination of prolonged hot, dry and windy conditions that leads to the biggest and most destructive fires. The model, called a self-organizing map (SOM), uses neural networks that mimic the processes of the human brain to study data sets, and over time, learns to recognize patterns. In this case, the map learned to recognize large-scale atmospheric pressure variables associated with hot, dry and windy weather on the surface.

SOMs have been successfully used in other meteorological studies, including efforts to broadly predict the impacts of climate change, but according to the researchers, this is the first time anyone has developed a SOM that could be used to make critical fire management decisions in realtime.

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