About this Engineering article

Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning by Ivan Vajs; Dejan Drajic; Nenad Gligoric; Ilija Radovanovic; Ivan Popovic is a Engineering article available to read on EtoBox.

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO~2~ and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensi

Author

Ivan Vajs; Dejan Drajic; Nenad Gligoric; Ilija Radovanovic; Ivan Popovic

Publisher

MDPI AG

Published

2021

Language

EN

Field

Engineering (Physical Sciences)