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JACIII Vol.29 No.1 pp. 138-151
doi: 10.20965/jaciii.2025.p0138
(2025)

Research Paper:

Spatiotemporal Interaction Based Dynamic Adversarial Adaptive Graph Neural Network for Air-Quality Prediction

Xiaoxia Chen*,†, Zhen Wang*, Hanzhong Xia*, Fangyan Dong**, and Kaoru Hirota***

*Faculty of Electrical Engineering and Computer Science, Ningbo University
No.818 Fenghua Road, Ningbo, Zhejiang 315211, China

Corresponding author

**Faculty of Mechanical Engineering and Mechanics, Ningbo University
No.818 Fenghua Road, Ningbo, Zhejiang 315211, China

***School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 100081, China

Received:
July 22, 2024
Accepted:
November 3, 2024
Published:
January 20, 2025
Keywords:
air quality prediction, spatiotemporal neural network, graph neural network, spatial-temporal interaction, dynamic adversarial adaptive graph
Abstract

Air quality issues have become a major environmental concern, with severe air pollution significantly reducing air quality and posing threats to human health. Accurate air quality prediction is crucial for preventing individuals from suffering the detrimental effects of severe air pollution. Recently, deep learning methods based on spatiotemporal graph neural networks (GNNs) have made considerable progress in modeling the temporal and spatial dependencies within air quality data by integrating GNNs with sequential models. Unfortunately, previous work often treats temporal and spatial dependencies as independent components, neglecting the intricate interactions between them. This oversight prevents the models from fully exploiting the complex spatiotemporal dependencies in the data, adversely affecting their predictive performance. To address these issues, we propose a general spatiotemporal interaction framework for air quality prediction. This framework models the bidirectional interactions between temporal and spatial dependencies in a data-driven manner. Furthermore, we designed a spatiotemporal feature extraction module and a dynamic adversarial adaptive graph learning module based on this framework. We introduce the Spatial-Temporal Interaction based Dynamic Adversarial Adaptive Graph Neural Network, capable of capturing the complex interactions between spatiotemporal dependencies and learning the dynamic spatial topology among sites by incorporating the competitive optimization concept of generative adversarial networks. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed method, outperforming existing baseline models.

Cite this article as:
X. Chen, Z. Wang, H. Xia, F. Dong, and K. Hirota, “Spatiotemporal Interaction Based Dynamic Adversarial Adaptive Graph Neural Network for Air-Quality Prediction,” J. Adv. Comput. Intell. Intell. Inform., Vol.29 No.1, pp. 138-151, 2025.
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Last updated on Feb. 07, 2025