New algorithm to predict final fire size at ‘moment of ignition’ developed

Scientists used a new technique to predict if the size of the wildfire from the moment it starts


By FireRescue1 Staff 

IRVING, Calif. — Scientists at the University of California, Irvine (UCI) have created an algorithm to determine the final size of a wildfire from the moment it ignites.

An interdisciplinary team of scientists used machine learning algorithms to forecast the size of a fire, according to UCI

In this photo provided by the Alaska Division of Forestry, smoke rises from a wildfire in east Anchorage, Alaska. (Photo/Jason Jordet,Alaska Division of Forestry via AP)
In this photo provided by the Alaska Division of Forestry, smoke rises from a wildfire in east Anchorage, Alaska. (Photo/Jason Jordet,Alaska Division of Forestry via AP)

"A useful analogy is to consider what makes something go viral in social media," Shane Coffield, a UCI doctoral student in Earth system science and the study’s lead author, said. "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."

By applying this type of thinking, researchers tested a hypothetical situation where dozens of fires break out simultaneously. 

"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 said.

The study focused on Alaska due to the number of wildfires over the past decade. UCI scientists fed the model climate data, atmospheric conditions and types of vegetation present. With that information, scientists were able to predict the size of the fire 50% of the time. 

Scientists noted two key variables in the study: the vapor pressure and the percentage of trees of the black spruce variety in Alaska. 

"Black spruce, which are dominant in Alaska, have these long, droopy branches that are designed – from an evolutionary perspective – to wick up fire," co-author Professor James Randerson said. "Their seeds are adapted to do well in a post-fire environment, so their strategy is to kill off everything else around them during a fire to reduce competition for their offspring."

A major advantage of using this tool is speed. The algorithm used in the experiment learns with each new data set point and assists the system in identifying the size of the fires quicker.

The study was published in the  International Journal of Wildland Fire.

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