Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators
For researchers and practitioners in wildfire smoke forecasting, this work provides a fast and accurate surrogate model that enables real-time predictions over long time scales.
The paper tackles the problem of computationally expensive wildfire smoke forecasting by introducing data-driven multilinear operators that map time-since-ignition to smoke concentration fields. The method achieves equal accuracy to Monte Carlo sampling with fewer than half the coupled model calls, and for smoke detection, it obtains 65% IoU and 0.95 AUC, significantly outperforming a prior classifier (0.15 IoU, 0.61 AUC).
Wildfires are a major producer of fine particulate matter, impacting human health and the electrical grid. Accurately forecasting smoke impacts over long time scales incorporates fuel treatment strategies, natural fuel succession, and stochastic events like lightning strikes. However, predicting smoke for each fuel distribution with a forward simulation of a coupled fire-atmosphere model is computationally infeasible. Moreover, relatively simple fire models are tractable to run in many long-time scenarios but do not capture smoke transport. We use data-driven multilinear operators to predict a smoke concentration field from knowledge of the time since ignition for two quantities of interest: aerosol optical depth and smoke detection. Our method first computes the principal components of time-since-ignition and smoke concentration fields and then learns a map from powers of the input coefficients to the output coefficients. We apply our learned operator to smoke prediction in the Upper Rio Grande Watershed. After collecting training data, learning the approximation weights on a CPU takes less than 30 seconds, and each forward call takes less than 1 ms. On a proxy for aerosol optical depth, we obtain equal accuracy to Monte Carlo sampling with fewer than half as many coupled model calls. For smoke detection, we obtain an intersection-over-union (IoU) of 65% and an area under the receiver operating characteristic curve (AUC) of 0.95 on holdout data. Our method is significantly more accurate than the most similar published smoke classifier, which obtains an IoU and AUC of 0.15 and 0.61, respectively, on a 2015 bushfire in Australia.