基于机器学习的城市空气温度集成低成本环境传感器的校准
Fang Nan1, Chao Zeng1, Huanfeng Shen1,2,3,4,5
1School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
低成本的传感器提供城市环境监测,但需要对数据质量进行校准. 这项研究表明,LightGBM机器学习显著提高了空气温度传感器数据的准确性,以改善公共卫生研究.
科学领域:
- 环境科学 环境科学
- 传感器技术 传感器技术
- 数据科学数据科学数据科学
背景情况:
- 低成本的传感器对于城市微环境监测至关重要,克服了传统网络的时空局限性.
- 对数据质量的担忧阻碍了低成本传感器在环境研究中的广泛采用.
- 现场校准对于提高数据可靠性和促进用户对传感器网络的信心至关重要.
研究的目的:
- 通过使用现场校准,提高来自低成本传感器的长期每小时空气温度数据的质量.
- 评估多重线性回归 (MLR) 和光梯度增强机 (LightGBM) 在传感器校准中的有效性.
- 调查空间距离和季节性变化对长期数据校准的影响.
主要方法:
- 在中国武汉利用物联网 (IoT) 应用程序进行现场传感器校准.
- 采用多重线性回归 (MLR) 和光梯度增强机 (LightGBM) 算法进行数据校准.
- 应用一次性交叉验证 (LOOCV) 用于严格的模型性能评估.
主要成果:
- 与MLR相比,LightGBM算法在校准传感器数据方面表现出更高的性能.
- 校准显著提高了数据质量,最差的传感器的R平方从0.416提高到0.957.
- 平均绝对误差 (MAE) 从6.255降至1.680,根平均平方误差 (RMSE) 从7.881降至2.148.8.
结论:
- 轻GBM是一种高效的先进机器学习方法,用于校准创新的低成本传感器.
- 这种校准方法大大提高了城市空气温度数据的准确性和可靠性.
- 该研究提供了一种可行的方法,用于获得高质量的实时数据,这对于城市环境和公共卫生研究至关重要.
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