Giving Compass' Take:

• Researchers report that machine learning could help predict the final size of a wildfire from the moment it starts. 

• Faced with a climate change-induced jump in the number of wildfires expected each season, state, county, and local firefighting authorities could benefit from some updated tools and techniques.

• What can we do about wildfires? Click here to find out. 

Built around a machine learning algorithm, the model can help forecast whether a wildfire will be small, medium, or large by the time it has run its course—knowledge useful to those in charge of allocating scarce firefighting resources.

“A useful analogy is to consider what makes something go viral in social media,” says lead author Shane Coffield, a doctoral student in earth system science at the University of California, Irvine. “We can think about what properties of a specific tweet or post might make it blow up and become really popular—and how you might predict that at the moment it’s posted or right before it’s posted.”

The researchers applied that thinking to a hypothetical situation in which dozens of fires break out simultaneously. It sounds extreme, but this scenario has become all too common in recent years in parts of the western United States as climate change has resulted in hot and dry conditions on the ground that can put a region at high risk of ignition.

“Only a few of those fires are going to get really big and account for most of the burned area, so we have this new approach that’s focused on identifying specific ignitions that pose the greatest risk of getting out of control,” Coffield says.

Read the full article on machine learning and wildfires by Brian Bell at Futurity.