用机器学习提升低成本传感器的二氧化碳测量精度。
Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point
- 采用随机森林回归校准低成本传感器数据。
- 校准后精度接近官方参考传感器水平。
- 适合预算有限但需长期监测的环境项目。
本文介绍了一种低成本、可快速重构的气候传感器系统，可灵活测量多种污染物。我们提出使用机器学习回归方法，将该低成本传感平台测得的二氧化碳数据，与南非气象局开普敦观测站的参考传感器数据进行校准。实验表明，在此场景下随机森林回归表现最佳。结果证明，机器学习方法可有效提升低成本传感器平台的性能，可能延长传感器网络人工校准的时间间隔。
原文摘要 · Abstract (English)
In this paper, we describe the design of an inexpensive and agile climate sensor system which can be repurposed easily to measure various pollutants. We also propose the use of machine learning regression methods to calibrate CO2 data from this cost-effective sensing platform to a reference sensor at the South African Weather Service's Cape Point measurement facility. We show the performance of these methods and found that Random Forest Regression was the best in this scenario. This shows that these machine learning methods can be used to improve the performance of cost-effective sensor platforms and possibly extend the time between manual calibration of sensor networks.
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