238 citations · 273 across the 4 of their papers we have counts for
4 papers
Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior
John Burge, Matthew R. Bonanni, R. Lily Hu +1
The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer…
Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics
John Burge, Matthew Bonanni, Matthias Ihme +1
As the climate changes, the severity of wildland fires is expected to worsen. Models that accurately capture fire propagation dynamics greatly help efforts for understanding, respo…
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Fantine Huot, R. Lily Hu, Matthias Ihme +6
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly…
Machine Learning for Precipitation Nowcasting from Radar Images
Shreya Agrawal, Luke Barrington, Carla Bromberg +3
High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather. As Deep Learning (DL) techniques have shown dra…