부산대학교 도서관

In this article, we present the statistical analysis of data from inexpensive sensors. We also present the performance of machine learning algorithms when used for automatic calibration of such sensors. In this, we have used low-cost nondispersive infrared CO2 sensor placed at a co-located site at Cape Point, South Africa (maintained by Weather South Africa). The collected low-cost sensor data and site truth data are investigated and compared. We compare and investigate the performance of random forest regression, support vector regression, 1-D convolutional neural network (CNN), and 1D-CNN long short-term memory network models as a method for automatic calibration and the statistical properties of these model predictions. In addition, we also investigate the drift in performance of these algorithms with time.