3 papers
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…
cs.LG2026
Time-Warping Recurrent Neural Networks for Transfer Learning
Jonathon Hirschi
Dynamical systems describe how a physical system evolves over time. Physical processes can evolve faster or slower in different environmental conditions. We use time-warping as res…
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…