Custom Loss Functions in Fuel Moisture Modeling

TL;DR

This paper investigates the use of custom loss functions in machine learning models for fuel moisture content prediction, aiming to improve wildfire spread forecasts by emphasizing dry fuels, with modest success.

Contribution

It introduces and evaluates custom loss functions that prioritize dry fuels in FMC modeling, a novel approach in wildfire prediction models.

Findings

Custom loss functions slightly improved ROS forecast accuracy.

Models with custom loss functions better capture nonlinear FMC-ROS relationship.

Further research needed to confirm real-time wildfire simulation improvements.

Abstract

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.