AI RESEARCH
Transfer Learning for Dead Fuel Moisture Prediction Using Time-Warping Recurrent Neural Networks
arXiv CS.LG
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ArXi:2605.08379v1 Announce Type: cross 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.