Giving Compass' Take:

• A team of researchers is using a "decision-tree" algorithm to analyze climate data and atmospheric conditions that will help predict the severity of wildfires. 

• How can donors keep supporting this type of research to advance disaster preparedness efforts? How can this technology help inform communities at risk?

• Read about wildfire recovery plans. 

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.

The team used Alaska as a study area for the project because a rash of concurrent fires in its boreal forests has plagued the state over the past decade, threatening human health and vulnerable ecosystems.

At the core of the new model is a “decision tree” algorithm. By feeding it climate data and crucial details about atmospheric conditions and the types of vegetation present around the starting point of a fire, the researchers could predict the final size of a blaze 50% of the time. A key variable is the vapor pressure deficit—just how little moisture there is in the area—during the first six days of a fire’s existence. A second major consideration for Alaskan forests is the percentage of trees of the black spruce variety.

One advantage of this new method is speed, Coffield says. The algorithm “learns” with each new data point and can quickly figure out the critical thresholds for identifying large fires. It’s possible for people to do this manually or by running simulations on each different ignition, he says, but the machine learning system’s statistical approach is “really much faster and more efficient, especially for considering multiple fires simultaneously.”

Read the full article about using machine learning to predict wildfires by Brian Bell at Futurity.