New machine learning algorithms could soon help firefighters forecast dangerous flashover ignition events using sensor data from burning buildings. Called P-Flash, the system was developed by Thomas Cleary and colleagues at the National Institute of Standards and Technology (NIST) in the US and Hong Kong Polytechnic University. Trained using data from thousands of simulated fires, the model can predict some flashovers in housefires up to 30 s before they occur.
Flashovers are among the most hazardous threats faced by firefighters. At high temperatures, all exposed combustible material in a room can be ignited simultaneously, releasing a huge amount of energy. To avoid danger, while maximizing the amount of time spent searching a fire for victims, it is critical for firefighters to predict these events as far in advance as possible.