具有适应边缘属性的动态图形神经网络用于空气质量预测:中国的一个案例研究
Jing Xu1, Shuo Wang1,2,3, Na Ying4
1School of Systems Science, Beijing Normal University, Beijing, 100875, China.
Heliyon
|July 17, 2023
概括
本研究引入了一个带有自适应边缘属性 (DGN-AEA) 的动态图神经网络,用于改进空气质量预测. 该模型以动态的方式学习空间关系,通过避免依赖预定义的结构来超越以前的方法.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 空气质量预测是一个复杂的时空模型挑战.
- 现有的方法往往单独处理空间和时间的依赖性.
- 循环神经网络 (RNN) 忽视空间信息,而图形卷积网络 (GCN) 则需要预定义的空间结构.
研究的目的:
- 开发一种用于准确预测空气质量的新型模型.
- 克服现有的方法的局限性,这些方法依赖于空间关系的先前信息.
- 在没有人类预定义结构的情况下,动态学习空间依赖.
主要方法:
- 提出了一个具有适应边缘属性 (DGN-AEA) 的动态图形神经网络.
- 使用消息传递网络生成一个自适应的双向动态图.
- 通过端到端的培训,学习边缘属性作为模型参数,消除了对先前信息的需求.
主要成果:
- 与基线模型相比,DGN-AEA模型实现了最先进的性能.
- 该模型成功地学习了无需先前结构输入的自适应边缘信息.
- 作为一个有价值的副产品,确定了站点之间的隐藏结构信息.
结论:
- DGN-AEA模型为时空空气质量预测提供了更有效的方法.
- 动态图形学习消除了对空间相关性进行手动特征工程的需要.
- 该模型发现隐藏结构的能力有助于决策分析.
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