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stat.AP2026
Transfer Learning for Dead Fuel Moisture Prediction Using Time-Warping Recurrent Neural Networks
Jonathon Hirschi, Jan Mandel, Adam Kochanski
This paper proposes a time-warping transfer learning method, a technique for temporally rescaling the learned dynamics of a recurrent neural network (RNN) with a Long Short-Term Me…
stat.AP2025
Custom Loss Functions in Fuel Moisture Modeling
Jonathon Hirschi
Fuel moisture content (FMC) is a key predictor for wildfire rate of spread (ROS). Machine learning models of FMC are being used more in recent years, augmenting or replacing tradit…