Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Emerging Science Journal
Srisang, Wacharapong
Unknown Affiliation
Environmental Science

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PM2.5 IoT Sensor Calibration and Implementation Issues Including Machine Learning Srisang, Wacharapong; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Khongthong, Chonthicha; Piamonte, John Rex P.; Sparrow, Elena B.
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-08

Abstract

Affordable IoT PM2.5 sensors, enabled by the Internet of Things, offer new ways to monitor air quality. However, concerns exist about their data accuracy. This study aimed (1) to investigate the low-cost PM sensor's performance under various outdoor ambient circumstances and (2) to evaluate seven calibration methods, which include decision trees, gradient-boosted trees, linear regression, nearest neighbors, neural networks, random forests, and the Gaussian Process. The Davis AirLink was used as a reference to compare the Plantower PMS3003 sensor's performance. The data from the Plantower PMS3003 sensor were then compared to the Davis AirLink values using calibration curves created by machine learning algorithms. Calibration curves were generated using machine learning algorithms trained on sensor measurements collected in two Thai cities (Nakhon Si Thammarat and Phuket). Our results show that all machine learning methods outperformed traditional linear regression, with decision trees and neural networks demonstrating the most significant improvement. This research highlights the need for sensor calibration and the limitations of current calibration methods and paves the way for advancements in cloud-based calibration and machine learning for improved data accuracy in IoT PM2.5sensor technology. Doi: 10.28991/ESJ-2024-08-06-08 Full Text: PDF
Co-Authors Jaroensutasinee, Krisanadej Jaroensutasinee, Mullica Khongthong, Chonthicha Piamonte, John Rex P. Sparrow, Elena B.