Interpretable Air Pollution Forecasting by Physics-Guided Spatiotemporal Decoupling
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911391320899584 |
|---|---|
| author | Zhang, Zhiguo Ma, Xiaoliang Schlesinger, Daniel |
| author_facet | Zhang, Zhiguo Ma, Xiaoliang Schlesinger, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Air Pollution Forecasting by Physics-Guided Spatiotemporal Decoupling Zhang, Zhiguo Ma, Xiaoliang Schlesinger, Daniel Machine Learning Artificial Intelligence 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. |
| title | Interpretable Air Pollution Forecasting by Physics-Guided Spatiotemporal Decoupling |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.20257 |