Articles | Volume 13, issue 4
Atmos. Meas. Tech., 13, 1693–1707, 2020
https://doi.org/10.5194/amt-13-1693-2020
Atmos. Meas. Tech., 13, 1693–1707, 2020
https://doi.org/10.5194/amt-13-1693-2020

Research article 07 Apr 2020

Research article | 07 Apr 2020

Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods

Minxing Si et al.

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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ke Du on behalf of the Authors (29 Feb 2020)  Author's response
ED: Publish as is (09 Mar 2020) by Keding Lu
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Short summary
The study evaluated the performance of a low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine-learning algorithms with random search techniques for the best model architectures. The two machine-learning algorithms are XGBoost and a feedforward neural network (NN).