Modeling Wildfire Perimeter Evolution using Deep Neural Networks
- URL: http://arxiv.org/abs/2009.03977v1
- Date: Tue, 8 Sep 2020 20:06:01 GMT
- Title: Modeling Wildfire Perimeter Evolution using Deep Neural Networks
- Authors: Maxfield E. Green, Karl Kaiser, Nat Shenton
- Abstract summary: We propose a wildfire spreadingmodel that predicts the evolution of the wildfire perimeter in 24 hour periods.
The model is able to learn wildfirespreading dynamics from real historic data sets from wildfires in the Western Sierra Nevada Mountains in Cal-ifornia.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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%
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