Abstract
Destructive wildfires result in billions of dollars
in damage each year and are expected to increase
in frequency, duration, and severity due to climate
change. The current state-of-the-art wildfire spread
models rely on mathematical growth predictions
and physics-based models, which are difficult and
computationally expensive to run. We present and
evaluate a novel system, FireCast. FireCast combines artificial intelligence (AI) techniques with
data collection strategies from geographic information systems (GIS). FireCast predicts which areas surrounding a burning wildfire have high-risk
of near-future wildfire spread, based on historical
fire data and using modest computational resources.
FireCast is compared to a random prediction model
and a commonly used wildfire spread model, Farsite, outperforming both with respect to total accuracy, recall, and F-score