针对干燃料设计特殊损失函数，提升野火蔓延预测精度。
Custom Loss Functions in Fuel Moisture Modeling
- 为干燃料增加损失权重，优化机器学习模型对燃料湿度的预测。
- 定制损失函数使野火蔓延速率预测准确率小幅提升。
- 适合关注野火模拟与灾害预警的研究者参考。
燃料含水量（FMC）是野火蔓延速率（ROS）的关键预测因子。近年来，机器学习模型在FMC预测中应用日益广泛，部分替代了传统物理模型。由于野火蔓延速率与燃料含水量之间存在高度非线性关系，微小的干燥燃料差异会导致蔓延速率显著变化。本研究测试了针对干燃料加权的定制损失函数，并在多种机器学习模型上评估其性能。通过时空交叉验证方法，检验定制损失函数是否提升野火蔓延速率的预测准确性。结果表明，定制损失函数使野火蔓延速率预测准确率略有提升。未来研究需进一步验证该改进能否带来更精确的实时野火模拟。
原文摘要 · Abstract (English)
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 traditional physics-based approaches. Wildfire rate of spread (ROS) has a highly nonlinear relationship with FMC, where small differences in dry fuels lead to large differences in ROS. In this study, custom loss functions that place more weight on dry fuels were examined with a variety of machine learning models of FMC. The models were evaluated with a spatiotemporal cross-validation procedure to examine whether the custom loss functions led to more accurate forecasts of ROS. Results show that the custom loss functions improved accuracy for ROS forecasts by a small amount. Further research would be needed to establish whether the improvement in ROS forecasts leads to more accurate real-time wildfire simulations.
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