Combined estimation of fire perimeters and fuel adjustment factors in FARSITE for forecasting wildland fire propagation

Abstract

As bias and uncertainties inevitably exist on both wildland fire model states and parameters, fire simulations do not always accurately forecast the temporal and spatial progression of wildfires. In this paper, a novel approach is proposed to estimate fire perimeters and fuel adjustment factors simultaneously for FARSITE tool. Fire perimeters estimation is the key to reduce model state bias, while fuel adjustment factors estimation is an essential component to reduce model parameter uncertainties. For those purposes, ensemble transform Kalman filter algorithm with adaptive proposal is adopted for correcting the fire perimeters, Monte Carlo based radial basis function neural network (RBFNN) is used to estimate fuel adjustment factors. The proposed method is first evaluated on homogeneous fuels distributed over flat ground for an experimental grassfire, then an intensive validation study is done on heterogeneous fuels distributed over complex terrain for a fire accident corresponding to the 2018 California Camp Fire. Results show that combined estimation of fire perimeters and fuel adjustment factors provides an interesting framework to produce accurate forecast of the fire propagation.