用机器学习校准低成本传感器，提升环境监测精度。
Statistical Study of Sensor Data and Investigation of ML-based Calibration Algorithms for Inexpensive Sensor Modules: Experiments from Cape Point
- 采用随机森林、SVM等模型自动校准低成本红外二氧化碳传感器。
- 1D-CNN-LSTM模型在长期校准中表现最佳，误差低于15%。
- 发现算法性能随时间漂移，适合需要持续校准的实地场景。
本文对南非开普敦附近的低成本非分散红外二氧化碳传感器数据进行了统计分析，并评估了多种机器学习算法在自动校准中的表现。实验基于气象南非公司维护的科珀点站点的共址数据，对比了随机森林回归、支持向量回归、一维卷积神经网络及1D-CNN-LSTM模型的校准效果。结果表明，1D-CNN-LSTM模型在预测精度上最优，平均绝对误差低于15%。同时，研究揭示了各算法随时间推移的性能漂移现象，强调了持续校准的重要性。该工作为低成本传感器在真实环境中的可靠应用提供了数据支撑和方法参考。
原文摘要 · Abstract (English)
In this paper we present the statistical analysis of data from inexpensive sensors. We also present the performance of machine learning algorithms when used for automatic calibration such sensors. In this we have used low-cost Non-Dispersive Infrared CO$_2$ 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, 1D Convolutional Neural Network 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.
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