融合物理规律与可解释机制，提升空气质量预测精度与透明度
Interpretable Air Pollution Forecasting by Physics-Guided Spatiotemporal Decoupling
- 将时空变化分解为物理引导的传输模块和可解释注意力模块
- 在斯德哥尔摩数据集上多时序预测均优于现有模型
- 适合需要高可信度决策的环境管理与政策制定者
准确且可解释的空气污染预测对公共健康至关重要，但多数模型在性能与可解释性之间存在权衡。本文提出一种基于物理引导、可解释设计的时空学习框架。该模型将污染物浓度的时空行为分解为两个透明的加性模块：第一模块是基于风向与地理信息的物理引导传输核，具有方向性权重（平流）；第二模块是可解释注意力机制，学习局部响应并归因于特定历史滞后和外部驱动因子。在斯德哥尔摩地区综合数据集上的评估表明，该模型在多个预测时序上持续优于当前最优基线。其高性能与时空可解释性的结合，为实际应用中的空气质量运营管理提供了更可靠的支撑。
原文摘要 · Abstract (English)
Accurate and interpretable air pollution forecasting is crucial for public health, but most models face a trade-off between performance and interpretability. This study proposes a physics-guided, interpretable-by-design spatiotemporal learning framework. The model decomposes the spatiotemporal behavior of air pollutant concentrations into two transparent, additive modules. The first is a physics-guided transport kernel with directed weights conditioned on wind and geography (advection). The second is an explainable attention mechanism that learns local responses and attributes future concentrations to specific historical lags and exogenous drivers. Evaluated on a comprehensive dataset from the Stockholm region, our model consistently outperforms state-of-the-art baselines across multiple forecasting horizons. Our model's integration of high predictive performance and spatiotemporal interpretability provides a more reliable foundation for operational air-quality management in real-world applications.
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