arXiv:2606.11676v1 Announce Type: cross Abstract: Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this weakness, we introduce a hybrid deep-learning parameterized Probabilistic Cellular Automata (CA) framework implemented in JAX. Our approach employs a Multi-Scale Convolutional Neural Network to dynamically generate spatially varying parameters that govern fire-spread probability, wind alignment, and slope influence. This hybrid design captures complex, nonlinear environmental interactions while pres
The increasing frequency and intensity of wildfires globally, coupled with advancements in AI and high-performance computing frameworks like JAX, necessitate more sophisticated and dynamic predictive models.
Accurate wildfire prediction is critical for disaster management, resource allocation, and protecting lives and infrastructure, and this AI-powered approach offers a significant improvement over traditional methods.
Wildfire spread prediction gains a new level of dynamic accuracy by incorporating complex, nonlinear environmental interactions through AI, moving beyond rigid, low-dimensional parameters.
- · Firefighting agencies
- · Insurance companies
- · Forestry management
- · AI model developers
- · Legacy wildfire modeling software
- · Communities in high-risk wildfire zones (if not adopted)
More precise and timely wildfire containment strategies become possible, reducing immediate damage.
Improved predictive capabilities could lead to better land use planning and preventative measures in fire-prone regions.
The success of this hybrid AI model could spur its adaptation to other complex environmental prediction challenges, such as flood or drought forecasting.
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Read at arXiv cs.LG