A Recurrent Neural Network for Forecasting Dead Fuel Moisture Content with Inputs from Numerical Weather Models

This paper proposes a recurrent neural network (RNN) model of dead 10 h fuel moisture content (FMC) for real-time forecasting. Weather inputs to the RNN are forecasts from the High-Resolution Rapid Refresh (HRRR), a numerical weather model. Geographic predictors include longitude, latitude, and elevation. Forecast accuracy is estimated in a study that utilizes a spatiotemporal cross-validation scheme. The RNN is trained on HRRR forecasts and observed FMC from weather station sensors within the Rocky Mountain region in 2023, then used to forecast FMC at new locations for all of 2024. The model is evaluated using a 48 h forecast window. The forecasts are compared to observed data from FMC sensors that were not included in training. The accuracy of the RNN is compared to several common baseline methods, including a physics-based ordinary differential equation, an XGBoost machine learning model, and hourly climatology. The RNN shows substantial forecasting accuracy improvements over the baseline methods.