Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point
Year: 2023
Type: conference-paper
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.
Authors: Travis Barrett, Amit Kumar Mishra
Institution: University of Cape Town
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W4384158755