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

Si, Minxing; Xiong, Ying; Du, Shan; Du, Ke

doi:https://doi.org/10.5194/amt-13-1693-2020

Articles | Volume 13, issue 4

https://doi.org/10.5194/amt-13-1693-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-13-1693-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 4

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, Ying Xiong, Shan Du, and Ke Du

Data sets

Evaluation and Calibration of a Low-cost Particle Sensor in Ambient Conditions Using Machine Learning Methods M. Si https://doi.org/10.5281/zenodo.3473833

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).