FireCast: Leveraging Deep Learning to Predict Wildfire Spread

摘要

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, Far-site, outperforming both with respect to total accuracy, recall, and F-score.