粒度
空气质量指数
中国大陆
计算机科学
中国
空气污染
生计
变压器
数据科学
环境经济学
计量经济学
数据挖掘
地理
气象学
工程类
经济
农业
电压
考古
有机化学
化学
电气工程
操作系统
作者
Yuxuan Liang,Yutong Xia,Songyu Ke,Yiwei Wang,Quan Wen,Junbo Zhang,Yu Zheng,Roger Zimmermann
出处
期刊:Proceedings of the ... AAAI Conference on Artificial Intelligence
[Association for the Advancement of Artificial Intelligence (AAAI)]
日期:2023-06-26
卷期号:37 (12): 14329-14337
被引量:8
标识
DOI:10.1609/aaai.v37i12.26676
摘要
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic growth. Forecasting air quality has become an increasingly important endeavor with significant social impacts, especially in emerging countries. In this paper, we present a novel Transformer termed AirFormer to predict nationwide air quality in China, with an unprecedented fine spatial granularity covering thousands of locations. AirFormer decouples the learning process into two stages: 1) a bottom-up deterministic stage that contains two new types of self-attention mechanisms to efficiently learn spatio-temporal representations; 2) a top-down stochastic stage with latent variables to capture the intrinsic uncertainty of air quality data. We evaluate AirFormer with 4-year data from 1,085 stations in Chinese Mainland. Compared to prior models, AirFormer reduces prediction errors by 5%∼8% on 72-hour future predictions. Our source code is available at https://github.com/yoshall/airformer.
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