CO2 Sensor: Outlier Detection, Calibration and Prediction
Year: 2024
Type: dissertation
Abstract: This thesis addresses key challenges in detecting outliers, calibrating low-cost CO2 sensors, and predicting short-term CO2 levels in environmental monitoring. Low-cost sensors often exhibit accuracy drift and limited precision, reducing the reliability of the data they generate. To mitigate these issues, we explored advanced methodologies for outlier detection, sensor calibration, and short-term forecasting. Outlier detection is a primary focus because outliers can distort sensor data, leading to inaccurate analysis, removing them ensures more reliable results for future data processing, employing both ARIMA (AutoRegressive Integrated Moving Average) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) methods. ARIMA effectively identified temporal anomalies through residual analysis, while DBSCAN handled spatial clustering-based outlier detection. Evaluation using ROC curve analysis confirmed the robustness of these methods, significantly enhancing the quality and reliability of sensor data. For sensor calibration, the thesis introduces the Deep Calibration Method (DeepCM), utilizing deep learning to model complex, non-linear sensor behaviors. Within this framework, the Sliding Window Supervised Calibration method achieved the best results, with a Root Mean Square Error (RMSE) of 4.56 ppm, significantly enhancing calibration accuracy compared to other methods. In the short-term prediction of CO2 levels, various Long Short-Term Memory (LSTM) models were employed, including the Single-sensor LSTM Model, Single-sensor LSTM Encoder-Decoder Model, and Multi-sensor LSTM Encoder-Decoder Model. The Single-sensor LSTM Model performed notably well, achieving an RMSE of 2.19 ppm and a Mean Absolute Error (MAE) of 1.64 ppm, demonstrating superior prediction accuracy over traditional time-series models. Overall, this work emphasizes the importance of robust outlier detection, accurate calibration, and effective short-term forecasting in enhancing the performance of low- cost CO2 sensors for real-world environmental monitoring applications.
Author: Li, Jike
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W6990031577