Optimization and Evaluation of Calibration for Low-cost Air Quality Sensors: Supervised and Unsupervised Machine Learning Models
Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 255–258 (2021)
Abstract. In this paper, data from five air monitoring stations in Sofia were compared to data from fixed low-cost PM sensors. A two-step model was created to refine the calibration process for low-cost PM sensors. At first, we calibrated the sensors with five separate supervised machine learning models and then the ANN-final model with anomaly detection completed the results. The ANN-final model improved the R2 values of the PM10 determined by low-cost sensors from 0.62 to 0.95 as compared to normal instruments. In conclusion, the two-step calibration model proved to be a positive solution to addressing low-cost sensor efficiency issues.
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