Effective Monitoring of an air quality network Conference Paper

abstract

• 2018 IEEE. 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.

name of conference

• 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

authors

• Nounou, Mohamed

published proceedings

• 2018 4TH INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR SIGNAL AND IMAGE PROCESSING (ATSIP)

author list (cited authors)

• Baklouti, R., Ben Hamida, A., Mansouri, M., Harkat, M., Nounou, M., & Nounou, H.

citation count

• 2

complete list of authors

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

publication date

• March 2018

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Air Quality Monitoring Network
• Exponentially Weighted Moving Average
• Fault Detection
• Generalized Likelihood Ratio Test
• Nonlinear Principal Component Analysis

Digital Object Identifier (DOI)

• 10.1109/atsip.2018.8364488

International Standard Book Number (ISBN) 13

• 9781538652398

start page

• 1

end page

• 4

URL

• http://dx.doi.org/10.1109/atsip.2018.8364488

user-defined tag

• 11 Sustainable Cities and Communities