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使用机器学习对低成本甲传感器进行校准

Hazel Louise Mitchell¹, Simon J Cox¹, Hugh G Lewis¹

¹Computational Engineering and Design Group, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK.

Sensors (Basel, Switzerland)

|February 24, 2024

概括

机器学习对低成本的甲传感器进行校准,用于环境监测. 这使得可以准确地测量温室气体排放在偏远地区,如泥炭地,即使在低度.

科学领域:

环境科学环境科学
传感器技术传感器技术
机器学习机器学习

背景情况:

温室气体排放需要了解它们的来源才能有效减轻.
低成本的无线传感器为偏远地区 (如泥炭地) 的环境监测提供了一种方法.
菲加罗NGM2611-E13甲传感器对户外环境中典型的低度的校准数据有限.

研究的目的:

通过机器学习,研究校准Figaro NGM2611-E13甲传感器用于低度测量 (0-200 ppm) 的可行性.
开发和评估机器学习模型,以考虑温度和湿度等环境变量.

主要方法:

在超过5万个校准数据点上训练了机器学习模型.
包括甲度从0-200ppm,温度从5-30°C,相对湿度从40-80%.
纳入变量之间的交互术语,以提高模型性能.

主要成果:

最终的机器学习模型实现了 5.1 ppm 的根平均平方误差 (RMSE).
该模型显示了高的确定系数 (R2),达到0.997.
结果表明,成功校准了低于200ppm的甲度.

结论:

当用机器学习校准时,Figaro NGM2611-E13传感器显示出测量低度甲的巨大潜力.
这种方法可以在具有挑战性的环境中加强对温室气体排放的环境监测.
准确的低度甲检测对于理解和应对气候变化至关重要.

关键词:

校准校准的时间机器学习是机器学习.甲是一种甲.传感器传感器传感器

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