Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model
Aerial wildfire suppression requires decisions about when, where, and how to deploy limited aircraft. We present an intervention-design framework built on a frozen hybrid convolutional neural network and cellular automaton (CNN-CA) simulator trained jointly on six historical wildfires. First, we jointly optimize binary drop execution and continuous location and orientation, with aircraft-specific footprints, wind drift, and availability, turnaround, and grounded-day constraints. Second, we model water as an immediate transfer of burning probability to the unburned state and retardant as a persistent reduction in the fuel contribution to spread. Third, we remove drops by a rollout-verified...
| Authors | Ion Matei, Maksym Zhenirovskyy, Takuya Kurihana et al. |
|---|---|
| Primary category | eess.SY |
| Published | 11 June 2026 |
| Last updated at source | 20 September 2026 |
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