Evaluation of nine machine learning regression algorithms for calibration of low-cost PM2.5 sensor

Low-cost sensors (LCS) can construct a high spatial and temporal resolution PM2.5 network but are affected by environmental parameters such as relative humidity and temperature. The data generated by LCS are inaccurate and require calibration against a reference instrument. This study has applied ni...

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Bibliographic Details
Published in:Journal of aerosol science Vol. 157; p. 105809
Main Authors: Kumar, Vikas, Sahu, Manoranjan
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.09.2021
Subjects:
Calibration
Gradient boosting
Low-cost sensor
Machine learning
PM2.5
Regression algorithims
Low-cost sensor
Calibration
Regression algorithims
PM2.5
Machine learning
Gradient boosting
ISSN:0021-8502, 1879-1964
Online Access:Get full text
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Summary:Low-cost sensors (LCS) can construct a high spatial and temporal resolution PM2.5 network but are affected by environmental parameters such as relative humidity and temperature. The data generated by LCS are inaccurate and require calibration against a reference instrument. This study has applied nine machine learning (ML) regression algorithms for Plantower PMS 5003 LCS calibration and compared their performance. The nine ML algorithms applied in this study are: (a) Multiple Linear Regression (MLR); (b) Lasso regression (L1); (c) Ridge regression (L2); (d) Support Vector Regression (SVR); (e) k- Nearest Neighbour (kNN); (f) Multilayer Perceptron (MLP); (g) Regression Tree (RT); (h) Random Forest (RF); (i) Gradient Boosting (GB). The comparison exhibits that kNN, RF and GB have the best performance out of all the algorithms with train scores of 0.99 and test scores of 0.97, 0.96 and 0.95 respectively. This study validates the capability of ML algorithms for the calibration of LCS. [Display omitted] •Evaluated nine regression algorithms for low-cost sensor calibration.•Linear models are underfitting while regression tree is overfitting.•k- Nearest Neighbour and Random Forest are providing accurate results.•Gradient Boosting is also suited for low-cost sensor calibration.•Validates the capability of machine learning algorithms for calibration.
ISSN:0021-8502
1879-1964
DOI:10.1016/j.jaerosci.2021.105809