Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement

arXiv:2404.08523v1 Announce Type: new
Abstract: Over the past decades, the increase in both frequency and intensity of large-scale wildfires due to climate change has emerged as a significant natural threat. The pressing need to design resilient landscapes capable of withstanding such disasters has become paramount, requiring the development of advanced decision-support tools. Existing methodologies, including Mixed Integer Programming, Stochastic Optimization, and Network Theory, have proven effective but are hindered by computational demands, limiting their applicability.
In response to this challenge, …

abstract arxiv become change climate climate change cs.ai cs.lg design development disasters fire intensity natural placement prevention reinforcement reinforcement learning resilient scale threat type wildfires