面向地球系统时空基础模型风险控制的决策导向不确定性量化
Decision-Oriented Uncertainty Quantification for Risk Control in Earth System Spatiotemporal Foundation Models
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中文总结 AI 辅助
提出决策导向不确定性量化框架，将地球系统时空基础模型的预测不确定性转化为行动条件风险，经效用感知校准后选择行动，显著降低决策遗憾与漏报率并提升期望效用。
中文摘要 AI 辅助
地球系统建模正从任务特定的预测器转向具有通用时空表征能力的基础模型。尽管这些模型能够联合编码动态地球场、外部强迫和静态地理背景以进行多步预测，但仅凭准确的点预测或统计校准的区间，不足以应对极端天气预警、洪水控制、可再生能源调度和应急资源分配等高影响应用。实践中的关键在于，预测不确定性能否在特定行动、损失函数和风险偏好下转化为可靠的决策风险。我们提出了一种面向地球系统时空基础模型的决策导向不确定性量化框架。该框架生成未来状态的预测分布，并利用决策风险适配器将预测样本、决策背景和效用函数映射为行动条件风险。一个效用感知校准模块进一步在下游决策损失层面而非仅在预测值层面确保可靠性。校准后的风险随后用于选择预警、调度、检查或资源分配行动。与最强基线相比，所提方法将决策遗憾降低了18.7%，将漏报率从14.2%降至9.1%，并将期望效用提高了11.6%，同时保持了90.4%的预测覆盖率，并将决策校准误差从0.083降至0.047。这些结果表明，决策导向的不确定性量化能够提升地球系统基础模型在风险敏感应用中的稳健性和运营价值。
英文摘要
Earth system modeling is shifting from task-specific predictors toward foundation models with general spatiotemporal representation capabilities. Although these models can jointly encode dynamic Earth fields, external forcings, and static geographic context for multistep forecasting, accurate point predictions or statistically calibrated intervals alone are insufficient for high-impact applications such as extremeweather warning, flood control, renewable-energy dispatch, and emergency resource allocation. What matters in practice is whether predictive uncertainty can be translated into reliable decision risk under specific actions, loss functions, and risk preferences. We propose a decision-oriented uncertainty quantification framework for Earth system spatiotemporal foundation models. The framework produces predictive distributions of future states and uses a decision risk adapter to map forecast samples, decision context, and utility functions into action-conditional risks. A utility-aware calibration module further enforces reliability at the downstream decision-loss level rather than only at the forecast-value level. Calibrated risks are then used to select warning, dispatch, inspection, or resource-allocation actions. Compared with the strongest baseline, the proposed method reduces decision regret by 18.7%, lowers the missed-event rate from 14.2% to 9.1%, and improves expected utility by 11.6%, while maintaining 90.4% predictive coverage and reducing decision calibration error from 0.083 to 0.047. These results suggest that decision-oriented uncertainty quantification can improve the robustness and operational value of Earth system foundation models in risk-sensitive applications.
发表机构
- Vanderbilt University（范德堡大学）
- City University of New York（纽约城市大学）
- Yale University（耶鲁大学）
- Wyze Inc.（Wyze公司）
- Northeastern University（东北大学）
- University of Southern California（南加州大学）
- Southwest University（西南大学）
机构由 AI 辅助整理，请以论文原文为准。
