Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point

Travis Barrett, Amit Kumar Mishra

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

In this paper, we describe the design of an inexpensive and agile climate sensor system which can be repurposed easily to measure various pollutants. We also propose the use of machine learning regression methods to calibrate CO2 data from this cost-effective sensing platform to a reference sensor at the South African Weather Service's Cape Point measurement facility. We show the performance of these methods and found that Random Forest Regression was the best in this scenario. This shows that these machine learning methods can be used to improve the performance of cost- effective sensor platforms and possibly extend the time between manual calibration of sensor networks.

Original languageEnglish
Title of host publicationI2MTC 2023 - 2023 IEEE International Instrumentation and Measurement Technology Conference
Subtitle of host publicationRising Above Covid-19, Proceedings
PublisherIEEE Press
Number of pages5
ISBN (Electronic)9781665453837
DOIs
Publication statusPublished - 13 Jul 2023
Event2023 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2023 - Kuala Lumpur, Malaysia
Duration: 22 May 202325 May 2023

Publication series

NameConference Record - IEEE Instrumentation and Measurement Technology Conference
Volume2023-May
ISSN (Print)1091-5281

Conference

Conference2023 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2023
Country/TerritoryMalaysia
CityKuala Lumpur
Period22 May 202325 May 2023

Keywords

  • Environment Monitoring
  • Machine Learning
  • Random Forest
  • Sensor Calibration
  • SVR

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