[论文] Accelerating Wildfire Hazard Mapping Across Canada with an AI Surrogat...

论文概要

研究领域: ML 作者: Hager Radi Abdelwahed, Toluwaleke Olutayo, Chirag Goel, Jana Osea, Aldo Zaimi, Tom Swystun et al. 发布时间: 2026-10-24 arXiv: 2610.30170

中文摘要

加拿大山火活动加剧，需要高频次的全国灾害制图来指导燃料管理、土地利用规划、基础设施保护与疏散路线。但基于过程的野火模拟框架（BurnP3/BurnP3+）依赖计算密集的蒙特卡洛模拟，单个区域建模单元就要跑数天到数周。本文给出三项贡献：(a) 对照过程模拟器训练、微调与评估 AI 代理模型的标准化框架；(b) 基于全国尺度数据训练的多任务深度学习代理，以秒级成本模拟燃烧概率、平均火强与平均蔓延速度；(c) 领域专用指标与反事实评估协议，探测分布外泛化。模型融合地理空间数据（燃料、地形、着火概率）与区域天气，逼近 BurnP3+ 输出，在一致性、秩相关、误差与空间重叠指标上表现强劲，保留下游灾害分级，并对专家设定的反事实干预给出物理上合理的响应。代表性区域上代理实现 806 倍端到端 CPU 加速、仅占 BurnP3+ 13% 的内存。

原文摘要

Canada's increasing wildfire activity requires frequent national hazard mapping to guide proactive mitigation, including fuel management, land-use planning, infrastructure protection, and evacuation routing. However, process-based wildfire simulation frameworks (e.g., BurnP3 and BurnP3+) rely on computationally intensive Monte Carlo simulations to model burn probability, fire intensity, and rate of spread. These workflows can take days to weeks to process a single regional modelling unit. To overcome these computational bottlenecks, we present three contributions developed through collaboration between AI researchers and wildfire modelling experts: (a) a standardized framework for training, fine-tuning, and evaluating AI surrogate models against process-based wildfire simulators; (b) a multi-task deep learning surrogate trained on national-scale data to emulate burn probability, mean fire intensity, and mean rate of spread at a fraction of the computational cost (seconds); and (c) domain-specific metrics and counterfactual evaluation protocols to probe out-of-distribution generalization. Integrating geospatial data (fuel, terrain, ignition likelihood) and regional weather, our model approximates BurnP3+ simulation outputs, achieving strong performance across agreement, rank-correlation, error, and spatial-overlap metrics. It preserves downstream hazard classifications and produces physically plausible responses to expert-informed counterfactual interventions. On a representative region, the surrogate achieves 806-fold end-to-end CPU acceleration and uses 13% of BurnP3+'s host memory, facilitating rapid scenario analysis.

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*自动采集于 2026-10-24*

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