Modeling Wildfire Perimeter Evolution using Deep Neural Networks
This work addresses the need for accurate real-time wildfire front prediction to aid firefighting and forest management, representing a domain-specific incremental improvement.
The authors tackled the problem of predicting wildfire perimeter evolution over 24-hour periods by developing a deep convolutional neural network trained on remotely sensed atmospheric and environmental data from historic wildfires in the Western Sierra Nevada Mountains. They achieved validation accuracies of 78% to 98%, significantly outperforming historic alternatives.
With the increased size and frequency of wildfire eventsworldwide, accurate real-time prediction of evolving wildfirefronts is a crucial component of firefighting efforts and for-est management practices. We propose a wildfire spreadingmodel that predicts the evolution of the wildfire perimeter in24 hour periods. The fire spreading simulation is based ona deep convolutional neural network (CNN) that is trainedon remotely sensed atmospheric and environmental time se-ries data. We show that the model is able to learn wildfirespreading dynamics from real historic data sets from a seriesof wildfires in the Western Sierra Nevada Mountains in Cal-ifornia. We validate the model on a previously unseen wild-fire and produce realistic results that significantly outperformhistoric alternatives with validation accuracies ranging from78% - 98%