A generative model for surrogates of spatial-temporal wildfire
  nowcasting
Sibo Cheng Yike Guo Rossella Arcucci
Recent increase in wildfires worldwide has led to the need for real-time fire
nowcasting. Physics-driven models, such as cellular automata and computational
fluid dynamics can provide high-fidelity fire spread simulations but they are
computationally expensive and time-consuming. Much effort has been put into
developing machine learning models for fire prediction. However, these models
are often region-specific and require a substantial quantity of simulation data
for training purpose. This results in a significant amount of computational
effort for different ecoregions. In this work, a generative model is proposed
using a three-dimensional Vector-Quantized Variational Autoencoders to generate
spatial-temporal sequences of unseen wildfire burned areas in a given
ecoregion. The model is tested in the ecoregion of a recent massive wildfire
event in California, known as the Chimney fire. Numerical results show that the
model succeed in generating coherent and structured fire scenarios, taking into
account the impact from geophysical variables, such as vegetation and slope.
Generated data are also used to train a surrogate model for predicting wildfire
dissemination, which has been tested on both simulation data and the real
Chimney fire event.