Using a first-of-its-kind machine learning tool, researchers at Columbia University and New York University have come up with a way to forecast the risk of forest fires in any particular region of the western US months in advance, and they can do so within minutes as opposed to hours. The breakthrough approach – which uses mathematics to assess the problem of fire based on climate data and is the first to use machine learning to make seasonal forecasts on a monthly basis – was unveiled recently in SIAM News, a publication of Society for Industrial and Applied Mathematics (SIAM) and presented at the 2024 SIAM Conference on Mathematics of Planet Earth. According to the researchers, the tool’s creation was spurred by last summer’s record-breaking fire season, originating in Canada and lasting longer than normal, with larger burn areas and more severe fires that significantly impacted air quality in large parts of North America.