A Deep Learning Approach Using Graph Neural Networks for Anomaly Detection in Air Quality Data Considering Spatiotemporal Correlations

A Deep Learning Approach Using Graph Neural Networks for Anomaly Detection in Air Quality Data Considering Spatiotemporal Correlations

The ambient air pollution problem has become more severe as the social economy develops. Abnormal event detection in air quality data can prevent property loss and protect human health. The majority of existing anomaly detection models in air quality data are based on a single variable or a single m...

Full description

Bibliographic Details
Main Authors: Xiaoling Lin, Hongzhang Wang, Jing Guo, Gang Mei
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Anomaly detection
air quality
deep learning
spatiotemporal correlations
graph neural networks (GNN)
Online Access:https://ieeexplore.ieee.org/document/9877800/

Internet

https://ieeexplore.ieee.org/document/9877800/

Similar Items