Calibration of Integrated Low-Cost Environmental Sensors for Urban Air Temperature Based on Machine Learning

Monitoring urban microenvironments using low-cost sensors effectively addresses the spatiotemporal limitations of conventional monitoring networks. However, their widespread adoption is hindered by concerns regarding data quality. Calibrating these sensors is crucial for enabling their large-scale deployment and increasing confidence among researchers and users. This study focuses on an internet of things (IoT) application in Wuhan, China, aiming to enhance the quality of long-term hourly air temperature data collected by low-cost sensors through on-site calibration. Multiple linear regression (MLR) and light gradient boosting machine (LightGBM) algorithms were employed for calibration, with leave-one-out cross-validation (LOOCV) being used for model evaluation. Factors, such as multiple scenarios, spatial distances, and seasonal variations, were also examined for their influence on long-term data calibration. The experimental findings revealed that the LightGBM method consistently outperformed MLR. Calibration using this approach markedly improved the sensor data quality, with the R-squared (R²) value of the sensor with the poorest raw data increasing from 0.416 to 0.957, its mean absolute error (MAE) decreasing from 6.255 to 1.680, and its root mean square error (RMSE) being reduced from 7.881 to 2.148. This study demonstrates the application potential of using LightGBM as an advanced machine learning (ML) method in innovative low-cost sensors, thereby providing a method of obtaining high-quality and real-time information for urban environmental and public health research.