The growing frequency of wildfires globally has highlighted the importance of immediate fire forecasting. Traditional high-accuracy fire spread simulations, like cellular automata and computational fluid dynamics, are detailed but require extensive computational resources and time. Consequently, there has been a significant push towards developing machine learning-based fire prediction models. These models, while effective, tend to be specific to certain regions and demand a large volume of simulation data for training, leading to considerable computational demands across various ecoregions. In response, this study introduces a generative approach using three-dimensional Vector-Quantized Variational Autoencoders. This method is designed to create spatial-temporal sequences predicting the progression of future wildfires in specific ecoregions. The effectiveness of this model was evaluated in the context of the Chimney fire, a notable recent wildfire in California. The results demonstrate that the model effectively produces realistic and structured fire scenarios, incorporating influential geophysical factors like vegetation and terrain slope. Additionally, the data generated by this model were used to develop and train a surrogate model for wildfire spread prediction. This surrogate model was successfully validated using both simulated data and actual data from the Chimney fire incident.
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