Transfer Learning for Dead Fuel Moisture Prediction Using Time-Warping Recurrent Neural Networks
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 Memory (LSTM) layer to enable task transfer across fuel moisture classes. Fuel moisture content (FMC) is divided into idealized classes based on characteristic lag time. Large quantities of real-time data are available for 10h fuels from sensors on weather stations, but observations of other fuel classes are sparse in space and time. We use transfer learning to adapt an RNN pretrained on 10h FMC to predict FMC for 1h, 100h, and 1000h fuels. We validate this method using data from a landmark field study conducted i...
| Authors | Jonathon Hirschi, Jan Mandel, Adam Kochanski |
|---|---|
| Primary category | stat.AP |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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