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Weather and fuel—two leading wildfire culprits—are now in the crosshairs of a University of Alberta researcher hoping to use machine learning against them.
By leveraging artificial intelligence (AI) to sort and map reams of meteorological data, Mike Flannigan and co-researchers from the Canadian Forest Service and University of Waterloo want to better predict where forest fires could break out and take hold so firefighting agencies can plan ahead.
Tools - Calls - Fire - Communities - Flannigan
"We want to complement existing decision-making tools so they can make the best calls possible in dealing with fire to help protect communities," Flannigan said.
By pinpointing where wildfires are likely to take root, fire crews and helicopters can be efficiently deployed, Flannigan said. That advance warning is crucial when it can take from three to seven days to get outside resources to the fire, he noted.
Fire - Managers - Fire - Weather - Area
Fire managers would be able to predict severe fire weather in a particular area, determine whether the needed resources are available and then plan to have them on hand.
"For instance, if it's wet in Quebec, crews and planes can be sent to Alberta to deal with wildfires there," he said.
Flannigan - Potential - Network - Software - Patterns
Flannigan is exploring the potential of neural network software that processes weather patterns and variables like temperature, pressure, humidity and wind speed to create far more detailed maps than currently exist.
"Think of a baby who sees a human face: they start to distinguish ears, nose and eyes, and as they distinguish further, they can see even more detail like whiskers and sideburns," Flannigan said. Neural networks work similarly, to identify severe weather patterns in multiple layers of existing weather data.
Map - Areas - Fire - Weather
"It could be turned into a map that would identify vulnerable areas and what times the fire weather would be severe,"...
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