Deep Smooth Random Sampling and Association Attention for Air Quality Anomaly Detection

Peng Wang, Minhang Li, Xiaoying Zhi, Xiliang Liu (), Zhixiang He, Ziyue Di, Xiang Zhu, Yanchen Zhu, Wenqiong Cui, Wenyu Deng and Wenhan Fan
Additional contact information
Peng Wang: Key Laboratory of Data Science and Smart Education Ministry of Education, Hainan Normal University, Haikou 570203, China
Minhang Li: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Xiaoying Zhi: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Xiliang Liu: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Zhixiang He: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Ziyue Di: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Xiang Zhu: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Yanchen Zhu: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Wenqiong Cui: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Wenyu Deng: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Wenhan Fan: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Mathematics, 2024, vol. 12, issue 13, 1-21

Abstract: Real-time monitoring and timely warning of air quality are vital components of building livable cities and implementing the “Healthy China” strategy. Real-time, efficient, and accurate detection of air quality anomalies holds great significance. However, almost all existing methods for air quality anomaly detection often overlook the imbalanced distribution of data. In addition, many traditional methods cannot learn both pointwise representation and pairwise association, so they cannot solve complex features. This study proposes an anomaly detection method for air quality monitoring based on Deep Smooth Random Sampling and Association Attention in Transformer (DSRS-AAT). Firstly, based on the third geographical law, the more similar the geographical environment, the closer the geographical target features are. We cluster sites according to the surrounding geographic features to fully explore latent feature associations. Then, we employ Deep Smooth Random Sampling to rebalance the air quality datasets. Meanwhile, the Transformer with association attention considers both prior associations and series associations to distinguish anomaly patterns. Experiments are carried out with real data from 95 monitoring stations in Haikou City, China. Final results demonstrate that the proposed DSRS-AAT improves the effectiveness of anomaly detection and provides interpretability analysis for traceability, owing to a significant improvement with the baselines (OmniAnomaly, THOC, etc.). The proposed method effectively enhances the effectiveness of air quality anomaly detection and provides a reference value for real-time monitoring and early warning of urban air quality.

Keywords: air quality anomaly detection; imbalanced data processing; geographical third law; transformer; livable cities (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/13/2048/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/13/2048/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:13:p:2048-:d:1426272

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().