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arXiv Machine Learning By Ion Matei, Maksym Zhenirovskyy, Takuya Kurihana, Rohit Vupala, Anthony Wong

Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model

Read the original on arXiv Machine Learning →

The paper introduces a hybrid CNN‑cellular automaton (CNN‑CA) simulator for planning aerial wildfire suppression, trained on six historical fires. It jointly optimizes binary drop decisions and continuous aircraft trajectories while accounting for aircraft constraints, wind drift, and fuel reduction effects of water and retardant. The authors evaluate the method against random, tactical, greedy, and derivative‑free planners, showing significant reductions in simulated fire extent under a 2020 Bear Fire case study, though noting the results are conditional on the frozen simulator and not operational evidence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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