Researchers at USC have developed a new model that combines generative AI and satellite data to accurately forecast wildfire spread—a potential breakthrough in wildfire management and emergency response.

The model uses satellite data to track a wildfire’s real-time progression, then feeds this information into a sophisticated computer algorithm that can accurately forecast the fire’s likely path, intensity and growth rate.

Detailed in an early study proof published in Artificial Intelligence for the Earth Systems, the study comes as California and much of the western US continue to grapple with an increasingly severe wildfire season.

Training generative AI model

The researchers began by gathering historical wildfire data from high-resolution satellite images of past wildfires, tracking how each fire started, spread and was eventually contained. Their comprehensive analysis revealed patterns influenced by different factors like weather, fuel (for example, trees, brush, etc.) and terrain.

They then trained a generative AI-powered computer model known as a conditional Wasserstein Generative Adversarial Network, or cWGAN, to simulate how these factors influence how wildfires evolve over time. They taught the model to recognize patterns in the satellite images that match up with how wildfires spread in their model.

Anticipating future fire spread

They then tested the cWGAN model on real wildfires that occurred in California between 2020 and 2022 to see how well it predicted where the fire would spread.

“By studying how past fires behaved, we can create a model that anticipates how future fires might spread,” said Assad Oberai, Hughes Professor and Professor of Aerospace and Mechanical Engineering at USC Viterbi and co-author of the study, in a statement.

“Fuel-like grass, shrubs or trees ignites, leading to complex chemical reactions that generate heat and wind currents. Factors such as topography and weather also influence fire behavior. Fires don’t spread much in moist conditions but can move rapidly in dry conditions,” he said. “These are highly complex, chaotic and nonlinear processes. To model them accurately, you need to account for all these different factors. You need advanced computing.”

The research was funded by the Army Research Office, NASA and the Viterbi CURVE program.

Citation: Bryan Shaddy et al., 23 Apr 2024, Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts, Artificial Intelligence for the Earth Systems, https://journals.ametsoc.org/view/journals/aies/aop/AIES-D-23-0087.1/AIES-D-23-0087.1.xml (open access)

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