Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

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 ar...

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Bibliographic Details
Main Authors: Ivan Vajs, Dejan Drajic, Nenad Gligoric, Ilija Radovanovic, Ivan Popovic
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Sensors
Subjects:
air pollution measurements
low-cost sensors
calibration
machine learning
artificial neural network
temperature and relative humidity
Online Access:https://www.mdpi.com/1424-8220/21/10/3338

Internet

https://www.mdpi.com/1424-8220/21/10/3338

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