用扩散模型预测野火蔓延，生成多组合理场景。
A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
- 采用去噪扩散框架模拟野火多种可能蔓延路径。
- 输出多组预测结果，体现火灾发展的不确定性。
- 适合灾害预警与应急决策者使用。
得益于生成式AI的进展，计算机如今能够模拟复杂自然过程。本文首次提出基于去噪扩散模型的野火蔓延预测方法，该模型不生成单一确定结果，而是学习火灾在不同环境条件下的多种可能演化路径。相比传统确定性模型，该方法能有效捕捉野火动态中的固有不确定性，输出反映物理合理分布的预测集合。这种技术可提升野火行为预判的智能性、速度与可靠性，为风险评估和应急响应提供有力支持。
原文摘要 · Abstract (English)
Thanks to recent advances in generative AI, computers can now simulate realistic and complex natural processes. We apply this capability to predict how wildfires spread, a task made difficult by the unpredictable nature of fire and the variety of environmental conditions it depends on. In this study, We present the first denoising diffusion model for predicting wildfire spread, a new kind of AI framework that learns to simulate fires not just as one fixed outcome, but as a range of possible scenarios. By doing so, it accounts for the inherent uncertainty of wildfire dynamics, a feature that traditional models typically fail to represent. Unlike deterministic approaches that generate a single prediction, our model produces ensembles of forecasts that reflect physically meaningful distributions of where fire might go next. This technology could help us develop smarter, faster, and more reliable tools for anticipating wildfire behavior, aiding decision-makers in fire risk assessment and response planning.
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