Stochastic comparison of machine learning approaches to calibration of mobile air quality monitors

E. Esposito, S. De Vito, M. Salvato, G. Fattoruso, V. Bright, R.L. Jones, O. Popoola

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

Recently, the interest in the development of new pervasive or mobile implementations of air quality multisensor devices has significantly grown. New application opportunities appeared together with new challenges due to limitations in dealing with rapid pollutants concentrations transients both for static and mobile deployments. Sensors dynamic is one of the primary factor in limiting the capability of the device of estimating true concentration when it is rapidly changing. Researchers have proposed several approaches to these issues but none have been tested in real conditions. Furthermore, no performance comparison is currently available. In this contribution, we propose and compare different approaches to the calibration problem of novel fast air quality multisensing devices, using two datasets recorded in field. Machine learning architectures have been designed, optimized and tested in order to tackle the cross sensitivities issues and sensors inherent dynamic limitations to perform accurate prediction and uncertainty estimation. Comparison results shows the advantage of dynamic non linear architectures versus static linear ones with support vector regressors scoring best results.
Original languageEnglish
DOIs
Publication statusPublished - 2018
Event3rd National Conference on Sensors, 2016 - Rome, Italy
Duration: 1 Jan 2018 → …

Conference

Conference3rd National Conference on Sensors, 2016
CountryItaly
CityRome
Period1/1/18 → …

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All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

Esposito, E., De Vito, S., Salvato, M., Fattoruso, G., Bright, V., Jones, R. L., & Popoola, O. (2018). Stochastic comparison of machine learning approaches to calibration of mobile air quality monitors. Paper presented at 3rd National Conference on Sensors, 2016, Rome, Italy. https://doi.org/10.1007/978-3-319-55077-0_38