A satellite image of Alaska captured in August 2005 shows the extent of smoke coverage from wildfires in the state's boreal forests. The blazes are likely to become large in exceptionally hot and dry conditions and when there's a high percentage of black spruce trees in the affected areas – key factors in a new predictive model developed by UCI scientists. An interdisciplinary team of scientists at the University of California, Irvine has developed a new technique for predicting the final size of a wildfire from the moment of ignition. Built around a machine learning algorithm, the model can help in forecasting whether a blaze is going to be small, medium or large by the time it has run its course – knowledge useful to those in charge of allocating scarce firefighting resources. The researchers' work is highlighted in a study published today in the International Journal of Wildland Fire.

You are free to share this article under the Attribution 4.0 International license. A new technique can predict the final size of wildfires from the moment of ignition, researchers report. 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.