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 in Oklahoma that was used to calibrate the state-of-the-art Nelson fuel moisture model.

翻译：本文提出一种时间扭曲迁移学习方法，即通过长短时记忆（LSTM）层对循环神经网络（RNN）学习到的动态特性进行时间尺度重标定，从而实现在不同燃料湿度类别间的任务迁移。燃料湿度含量（FMC）根据特征滞后时间划分为理想化类别。气象站传感器可获取大量10小时燃料的实时数据，但其他燃料类别的观测数据在时空上均较为稀疏。我们利用迁移学习将基于10小时FMC预训练的RNN模型进行适配，使其能够预测1小时、100小时及1000小时燃料的FMC。通过俄克拉荷马州进行的里程碑式野外研究数据对方法进行验证，该数据曾被用于校准最先进的尼尔森燃料湿度模型。