| 论文题目： | Improving air quality assessment using physics-inspired deep graph learning |
|---|---|
| 第一作者： | Li Lianfa, Wang Jinfeng, Franklin Meredith, Yin Qian etc. |
| 联系作者： | |
| 发表年度： | 2023 |
| 摘 要： | Existing methods for fine-scale air quality assessment have significant gaps in their reliability. Purely data-driven methods lack any physically-based mechanisms to simulate the interactive process of air pollution, potentially leading to physically inconsistent or implausible results. Here, we report a hybrid multilevel graph neural network that encodes fluid physics to capture spatial and temporal dynamic characteristics of air pollutants. On a multi-air pollutant test in China, our method consistently improved extrapolation accuracy by an average of 11-22% compared to several baseline machine learning methods, and generated physically consistent spatiotemporal trends of air pollutants at fine spatial and temporal scales. |
| 英文摘要： | |
| 刊物名称： | NPJ CLIMATE AND ATMOSPHERIC SCIENCE |
| 全文链接： | |
| 论文类别： | SCI |
在线留言
联系我们
所长信箱