面向安全关键的野火疏散制图的符合性风险控制：表格、空间和图基模型的比较研究
Conformal Risk Control for Safety-Critical Wildfire Evacuation Mapping: A Comparative Study of Tabular, Spatial, and Graph-Based Models
AI总结：
本文提出首个应用符合性风险控制（CRC）于野火扩散预测，提供有限样本保证的FNR≤0.05，证明模型架构决定疏散效率，CRC决定安全，空间模型比LightGBM效率高4.2倍，图模型无显著效率提升。
摘要：
当今部署的每种野火预测模型都具有危险属性：这些方法均未提供正式保证，即它们遗漏的火势扩散量有多大。尽管已有大量使用深度学习进行野火扩散预测的工作，但此前没有研究将分布无关的安全保证应用于此领域，使疏散规划者依赖概率阈值而无正式保证。本文通过首次应用符合性风险控制（CRC）于野火扩散预测，提供有限样本保证的FNR≤0.05，揭示了一个明显失败：在三个模型家族中（表格：LightGBM，AUROC 0.854；卷积：Tiny U-Net，AUROC 0.969；图基：Hybrid ResGNN-UNet，AUROC 0.964），标准阈值仅捕捉7-72%的真实火势扩散。CRC消除此失败。我们的核心发现是模型架构决定疏散效率，而CRC决定安全：空间模型结合CRC可实现约95%的火势覆盖，同时仅标记约15%的总像素，比LightGBM效率高4.2倍，而图模型的额外复杂度相对于简单U-Net无显著效率提升。我们提出一种意识导向的三元CRC框架，为操作分级分配SAFE/MONITOR/EVACUATE区域，并表征在极端类别不平衡（约5%火势流行度）下的普及权重界限的根本限制。所有模型、校准代码和评估流程均公开以供可重复性。
英文摘要：
Every wildfire prediction model deployed today shares a dangerous property: none of these methods provides formal guarantees on how much fire spread is missed. Despite extensive work on wildfire spread prediction using deep learning, no prior study has applied distribution-free safety guarantees to this domain, leaving evacuation planners reliant on probability thresholds with no formal assurance. We address this gap by presenting, to our knowledge, the first application of conformal risk control (CRC) to wildfire spread prediction, providing finite-sample guarantees on false negative rate (FNR <= 0.05). We expose a stark failure: across three model families of increasing complexity (tabular: LightGBM, AUROC 0.854; convolutional: Tiny U-Net, AUROC 0.969; and graph-based: Hybrid ResGNN-UNet, AUROC 0.964), standard thresholds capture only 7-72% of true fire spread. CRC eliminates this failure uniformly. Our central finding is that model architecture determines evacuation efficiency, while CRC determines safety: both spatial models with CRC achieve approximately 95% fire coverage while flagging only approximately 15% of total pixels, making them 4.2x more efficient than LightGBM, while the graph model's additional complexity over a simple U-Net yields no meaningful efficiency gain. We propose a shift-aware three-way CRC framework that assigns SAFE/MONITOR/EVACUATE zones for operational triage, and characterize a fundamental limitation of prevalence-weighted bounds under extreme class imbalance (approximately 5% fire prevalence). All models, calibration code, and evaluation pipelines are released for reproducibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
