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气象因素的时空融合用于多个地点的PM2.5预测:深度学习和时间变量图表方法

Hongqing Wang1, Lifu Zhang2, Rong Wu3

  • 1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; University of Chinese Academy of Sciences, Beijing, 100049, China.

Environmental research
|October 5, 2023
PubMed
概括

本研究引入了一种深度学习多图形模型,通过分析多个地点和气象因素来预测细颗粒物 (PM2.5) 度. 这种新的方法提高了空气质量管理和对污染的理解.

关键词:
多个监测站点的联合影响.气象学因素 气象学因素预测PM2.5度的预测方法时空特征融合的空间时间特征融合.

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