Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network

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dc.citation.issue2
dc.citation.volume23
dc.contributor.authorAli S
dc.contributor.authorAlam F
dc.contributor.authorArif K
dc.contributor.authorPotgieter J-G
dc.coverage.spatialSwitzerland
dc.date.available2023-01-11
dc.date.available2023-01-06
dc.date.issued11/01/2023
dc.description.abstractThe advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.
dc.description.publication-statusPublished online
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/36679650
dc.identifiers23020854
dc.identifier.citationSensors (Basel), 2023, 23 (2)
dc.identifier.doi10.3390/s23020854
dc.identifier.eissn1424-8220
dc.identifier.elements-id458809
dc.identifier.harvestedMassey_Dark
dc.identifier.urihttps://hdl.handle.net/10179/17941
dc.languageeng
dc.publisherMDPI AG
dc.relation.isPartOfSensors (Basel)
dc.relation.urihttps://www.mdpi.com/1424-8220/23/2/854
dc.subject1DCNN
dc.subjectair quality monitoring
dc.subjectcalibration
dc.subjectlow-cost CO sensor
dc.subjectAir Pollutants
dc.subjectParticulate Matter
dc.subjectCalibration
dc.subjectEnvironmental Monitoring
dc.subjectAir Pollution
dc.subject.anzsrc0301 Analytical Chemistry
dc.subject.anzsrc0805 Distributed Computing
dc.subject.anzsrc0906 Electrical and Electronic Engineering
dc.subject.anzsrc0502 Environmental Science and Management
dc.subject.anzsrc0602 Ecology
dc.titleLow-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment/Agritech
pubs.organisational-group/Massey University/College of Sciences/School of Food and Advanced Technology
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