计算机科学
对抗制
机器学习
背景(考古学)
人工智能
符号
空气质量指数
图形
块(置换群论)
质量(理念)
任务(项目管理)
数据挖掘
理论计算机科学
数学
地理
几何学
气象学
管理
考古
经济
哲学
认识论
算术
作者
Jindong Han,Hao Liu,Hengshu Zhu,Hui Xiong
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-11-01
卷期号:35 (11): 11515-11528
被引量:6
标识
DOI:10.1109/tkde.2023.3236423
摘要
Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weather prediction, most of them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On one hand, the accurate prediction of one task can help improve another task's performance. On the other hand, geospatially distributed air quality and weather monitoring stations provide additional hints for city-wide spatiotemporal dependency modeling. Inspired by the above two insights, in this paper, we propose a multi-view multi-adversarial approach, entitled MasterGNN $^{+}$ , to jointly predict air quality and weather conditions. First, we devise a multi-view graph learning block to model spatial autocorrelation based on geographical distance and environmental context. Then, a dedicated evolved recurrent network is proposed to dynamically capture the long-range and independent temporal autocorrelation for each monitoring station and time slot. After that, we develop a multi-adversarial graph learning framework to against observation noise propagation introduced by spatiotemporal modeling. Moreover, we present an adaptive training strategy by formulating multi-adversarial learning as a multi-task learning problem. Finally, extensive experiments on two real-world datasets show that MasterGNN $^{+}$ achieves the best performance compared with seven baselines on both air quality and weather prediction tasks.
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