Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators

TL;DR

This paper introduces a data-driven multilinear operator approach for real-time wildfire smoke prediction, achieving high accuracy with minimal computational cost, and outperforming existing classifiers.

Contribution

The authors develop a novel multilinear operator method that enables fast, accurate smoke prediction from fire data, suitable for long-term wildfire impact forecasting.

Findings

Training weights computation takes less than 30 seconds on CPU.

Each smoke prediction call takes less than 1 millisecond.

Achieved IoU of 65% and AUC of 0.95 on holdout data.

Abstract

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.