Title :

Enhanced sensor environment graph based deep learning approach for air quality anomaly detection

Author(s) :

Masmoudi, Sahar [Auteur correspondant]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Centre for Energy and Environment [CERI EE - IMT Nord Europe]
Garnier, Christelle [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Savard, Anne [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Itier, Vincent [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sauvage, Stephane [Auteur]
Centre for Energy and Environment [CERI EE - IMT Nord Europe]
Bulot, Florentin [Auteur]
Kaluzny, Pascal [Auteur]

Conference title :

32 ND European Signal Processing Conference (EUSIPCO) 2024

City :

Lyon (Centre des Congrès)

Country :

France

Start date of the conference :

2024-08-26

Publication date :

2024-11-30

English keyword(s) :

Pollutant forecasting Graph neural network A3T-GCN Anomaly detection
Pollutant forecasting
Graph neural network
A3T-GCN
Anomaly detection

HAL domain(s) :

Sciences de l'environnement/Ingénierie de l'environnement
Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

<div><p>Air pollution is among the major threats to human well-being, highlighting the critical need for air quality monitoring, especially in urban areas. Whereas the development of lowcost pollution sensors has facilitated ...
Show more ><div><p>Air pollution is among the major threats to human well-being, highlighting the critical need for air quality monitoring, especially in urban areas. Whereas the development of lowcost pollution sensors has facilitated a widespread monitoring, a reliable anomaly detection system is required to properly characterize data for the end-users. In this paper, we propose an enhanced deep learning approach based on the A3T-GCN (Attention Temporal Graph Convolutional Network) model that accurately forecasts particulate matter PM2.5 concentrations using real past measurements from a deployed sensor network. Our proposed Enhanced-A3T-GCN embeds all the available spatial and temporal correlations within the sensor network, along with additional information regarding the sensor environment in a graph. It is shown to achieve significant performance improvement with respect to other deep learning forecasting methods, emphasizing the importance of exploiting the sensor environment-based information. Further, the achieved accurate forecasting makes it possible to detect anomalies injected at both single and multiple sensor levels.</p></div>Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Submission date :

2024-11-22T03:05:44Z