Imagine it is the dead of night and a wildfire starts without anyone present to witness its origin. Was it an ember from a campfire? A stray firework? A spark from a power line? A car backfiring? Arson? Currently there are 12 known forms of human-started ignition. The 13th is simply categorized as โunknownโ. Unfortunately, the origins of more than 50% of reported fires in recent years fall under this category, and thatโs a serious problem. Understanding the origins of a fire is essential for authorities and fire management teams to determine their response, especially as climate change makes wildfires a dangerous and expensive certainty. But what if someone could revolutionize wildfire categorization by harnessing machine learning and be able to provideโfor the first timeโthe probable cause of every unknown fire ignition? Enter Yavar Pourmohamad. Pourmohamad is a doctoral student in the School of Computing working alongside Civil Engineering faculty member Mojtaba Sadegh, and in January 2025, he published groundbreaking research highlighting the promise of machine learning in the battle against wildfire origin categorization.
