Effective monitoring of an air quality network

Raoudha Baklouti, Ahmed Ben Hamida, Majdi Mansouri, Mohamed Faouzi Harkat, Mohamed Nounou, Hazem Nounou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Air pollution in urban areas could be considered as one of the most dangerous types of pollution that can cause impact health and the ecosystem. Hence, monitoring air quality networks has captivated the interest of various research studies. In this context, this paper deals with Fault Detection of an Air Quality Monitoring Network. The proposed approach is based on nonlinear principal component analysis to cope with modeling of nonlinear data. In addition, the fault detection would be improved by combining exponentially weighted moving average with hypothesis testing technique: generalized likelihood ratio test. The evaluation was carried out on an Air Quality Monitoring Network (AQMN). The results revealed a good results compared to the classical PCA.

Original languageEnglish
Title of host publication2018 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538652398
DOIs
Publication statusPublished - 23 May 2018
Event4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018 - Sousse, Tunisia
Duration: 21 Mar 201824 Mar 2018

Other

Other4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018
CountryTunisia
CitySousse
Period21/3/1824/3/18

Fingerprint

Air quality
Fault detection
Monitoring
Air pollution
Principal component analysis
Ecosystems
Pollution
Health
Testing

Keywords

  • Air Quality Monitoring Network
  • Exponentially Weighted Moving Average
  • fault detection
  • Generalized Likelihood Ratio Test
  • Nonlinear principal component analysis

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

Cite this

Baklouti, R., Ben Hamida, A., Mansouri, M., Harkat, M. F., Nounou, M., & Nounou, H. (2018). Effective monitoring of an air quality network. In 2018 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018 (pp. 1-4). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ATSIP.2018.8364488

Effective monitoring of an air quality network. / Baklouti, Raoudha; Ben Hamida, Ahmed; Mansouri, Majdi; Harkat, Mohamed Faouzi; Nounou, Mohamed; Nounou, Hazem.

2018 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Baklouti, R, Ben Hamida, A, Mansouri, M, Harkat, MF, Nounou, M & Nounou, H 2018, Effective monitoring of an air quality network. in 2018 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018. Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018, Sousse, Tunisia, 21/3/18. https://doi.org/10.1109/ATSIP.2018.8364488
Baklouti R, Ben Hamida A, Mansouri M, Harkat MF, Nounou M, Nounou H. Effective monitoring of an air quality network. In 2018 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-4 https://doi.org/10.1109/ATSIP.2018.8364488
Baklouti, Raoudha ; Ben Hamida, Ahmed ; Mansouri, Majdi ; Harkat, Mohamed Faouzi ; Nounou, Mohamed ; Nounou, Hazem. / Effective monitoring of an air quality network. 2018 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-4
@inproceedings{5cf4d0544f294ea094f9f6ad799aeea0,
title = "Effective monitoring of an air quality network",
abstract = "Air pollution in urban areas could be considered as one of the most dangerous types of pollution that can cause impact health and the ecosystem. Hence, monitoring air quality networks has captivated the interest of various research studies. In this context, this paper deals with Fault Detection of an Air Quality Monitoring Network. The proposed approach is based on nonlinear principal component analysis to cope with modeling of nonlinear data. In addition, the fault detection would be improved by combining exponentially weighted moving average with hypothesis testing technique: generalized likelihood ratio test. The evaluation was carried out on an Air Quality Monitoring Network (AQMN). The results revealed a good results compared to the classical PCA.",
keywords = "Air Quality Monitoring Network, Exponentially Weighted Moving Average, fault detection, Generalized Likelihood Ratio Test, Nonlinear principal component analysis",
author = "Raoudha Baklouti and {Ben Hamida}, Ahmed and Majdi Mansouri and Harkat, {Mohamed Faouzi} and Mohamed Nounou and Hazem Nounou",
year = "2018",
month = "5",
day = "23",
doi = "10.1109/ATSIP.2018.8364488",
language = "English",
pages = "1--4",
booktitle = "2018 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Effective monitoring of an air quality network

AU - Baklouti, Raoudha

AU - Ben Hamida, Ahmed

AU - Mansouri, Majdi

AU - Harkat, Mohamed Faouzi

AU - Nounou, Mohamed

AU - Nounou, Hazem

PY - 2018/5/23

Y1 - 2018/5/23

N2 - Air pollution in urban areas could be considered as one of the most dangerous types of pollution that can cause impact health and the ecosystem. Hence, monitoring air quality networks has captivated the interest of various research studies. In this context, this paper deals with Fault Detection of an Air Quality Monitoring Network. The proposed approach is based on nonlinear principal component analysis to cope with modeling of nonlinear data. In addition, the fault detection would be improved by combining exponentially weighted moving average with hypothesis testing technique: generalized likelihood ratio test. The evaluation was carried out on an Air Quality Monitoring Network (AQMN). The results revealed a good results compared to the classical PCA.

AB - Air pollution in urban areas could be considered as one of the most dangerous types of pollution that can cause impact health and the ecosystem. Hence, monitoring air quality networks has captivated the interest of various research studies. In this context, this paper deals with Fault Detection of an Air Quality Monitoring Network. The proposed approach is based on nonlinear principal component analysis to cope with modeling of nonlinear data. In addition, the fault detection would be improved by combining exponentially weighted moving average with hypothesis testing technique: generalized likelihood ratio test. The evaluation was carried out on an Air Quality Monitoring Network (AQMN). The results revealed a good results compared to the classical PCA.

KW - Air Quality Monitoring Network

KW - Exponentially Weighted Moving Average

KW - fault detection

KW - Generalized Likelihood Ratio Test

KW - Nonlinear principal component analysis

UR - http://www.scopus.com/inward/record.url?scp=85048506531&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048506531&partnerID=8YFLogxK

U2 - 10.1109/ATSIP.2018.8364488

DO - 10.1109/ATSIP.2018.8364488

M3 - Conference contribution

SP - 1

EP - 4

BT - 2018 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -