融合物理模型与机器学习，精准预测北极冻土退化对基础设施的风险。
Hybrid Physics-ML Framework for Pan-Arctic Permafrost Infrastructure Risk at Record 2.9-Million Observation Scale
- 用290万条观测数据训练集成模型，结合气候与冻土分布信息。
- 在升温5℃情景下预测冻土比例平均下降20.3个百分点，超半数俄属北极区损失超20%。
- 提供可开源使用的风险地图和不确定性评估，适合工程与气候规划者使用。
北极变暖威胁着北纬地区超过1000亿美元依赖冻土的基础设施，但现有风险评估缺乏时空验证、不确定性量化及决策支持能力。我们提出一种混合物理-机器学习框架，整合2005至2021年间来自171,605个地点的290万条观测数据（含冻土占比与气候再分析数据）。采用随机森林+直方图梯度提升+弹性网络的堆叠集成模型，在严格的时空交叉验证下达到R²=0.980（RMSE=5.01 pp），有效防止数据泄露。为克服纯机器学习在情景外推中的局限，构建了60%基于学习的气候-冻土关系与40%物理冻土敏感性模型（-10 pp/℃）相结合的混合方法。在RCP8.5情景（未来10年升温+5℃）下，预测冻土占比平均下降20.3个百分点（中位数：-20.0 pp），51.5%的俄属北极区域损失超过20个百分点。基础设施风险分类识别出15%高风险区（25%中风险），并生成空间显式的不确定性地图。本框架为全球最大规模经验证的冻土机器学习数据集，首次实现面向北极基础设施的可操作混合物理-机器学习预测系统，并提供开源工具，支持工程设计规范与气候适应规划中的概率化冻土预测。该方法可推广至其他冻土区，展示混合范式如何突破纯数据驱动在气候变化应用中的瓶颈。
原文摘要 · Abstract (English)
Arctic warming threatens over 100 billion in permafrost-dependent infrastructure across Northern territories, yet existing risk assessment frameworks lack spatiotemporal validation, uncertainty quantification, and operational decision-support capabilities. We present a hybrid physics-machine learning framework integrating 2.9 million observations from 171,605 locations (2005-2021) combining permafrost fraction data with climate reanalysis. Our stacked ensemble model (Random Forest + Histogram Gradient Boosting + Elastic Net) achieves R2=0.980 (RMSE=5.01 pp) with rigorous spatiotemporal cross-validation preventing data leakage. To address machine learning limitations in extrapolative climate scenarios, we develop a hybrid approach combining learned climate-permafrost relationships (60%) with physical permafrost sensitivity models (40%, -10 pp/C). Under RCP8.5 forcing (+5C over 10 years), we project mean permafrost fraction decline of -20.3 pp (median: -20.0 pp), with 51.5% of Arctic Russia experiencing over 20 percentage point loss. Infrastructure risk classification identifies 15% high-risk zones (25% medium-risk) with spatially explicit uncertainty maps. Our framework represents the largest validated permafrost ML dataset globally, provides the first operational hybrid physics-ML forecasting system for Arctic infrastructure, and delivers open-source tools enabling probabilistic permafrost projections for engineering design codes and climate adaptation planning. The methodology is generalizable to other permafrost regions and demonstrates how hybrid approaches can overcome pure data-driven limitations in climate change applications.
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