在低成本空气质量监测网络中缓解概念漂移
Gerardo D'Elia1,2, Matteo Ferro3, Paolo Sommella2
1TERIN-SSI-EDS Laboratory, ENEA CR-Portici, P. le E. Fermi 1, 80055 Portici, Italy.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
未来的空气质量监测使用机器学习进行传感器校准. 这项研究通过使用堆叠组合策略来增强NO2传感器校准,以减轻概念漂移,延长模型有效性和改善空气质量网络的数据质量.
科学领域:
- 环境科学 环境科学
- 传感器技术 传感器技术
- 机器学习应用 机器学习应用
背景情况:
- 未来的空气质量监测网络将依赖于使用机器学习校准的低成本传感器.
- 概念漂移是一个主要的挑战,导致操作机器学习模型中的数据质量下降.
- 准确的校准对于满足欧洲指令相对扩展不确定性 (REU) 极限至关重要.
研究的目的:
- 在概念漂移检测后解决低成本NO2传感器的校准模型更新问题.
- 确定模型更新的最佳数据,以确保符合REU限制.
- 评估不同校准模型和整体策略在缓解概念漂移方面的有效性.
主要方法:
- 研究了用于概念漂移缓解的一般/全球和重要性权重校准模型.
- 应用了将两种校准模型结合在一起的堆叠组合策略.
- 基于延长校准模型的时间有效性来评估模型性能.
主要成果:
- 独立地,无论是一般/全球或重要性权重模型都是不够的.
- 堆叠组合策略显著延长了所有测试的传感器的校准模型的时间有效期至少三个星期.
- 这种方法最大限度地提高了在初始共定位过程中收集的数据的实用性.
结论:
- 堆叠组合策略有效地减轻了低成本NO2传感器校准中的概念漂移.
- 这种方法提高了传感器校准模型的可靠性和寿命.
- 这些发现支持将先进的机器学习技术整合到可靠的空气质量监测中.
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