使用机器学习分析和预测大气臭氧度
Stephan Räss1,2, Markus C Leuenberger1,2
1Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland.
Frontiers in big data
|January 30, 2025
概括
机器学习使用空气质量数据准确预测大气臭氧度. 非线性模型比人工神经网络更简单,预测误差最低,证明了ML.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 数据科学数据科学数据科学
背景情况:
- 大气中的臭氧化学是复杂的,涉及许多物质和反应.
- 准确预测臭氧度对于空气质量管理至关重要.
- 机器学习 (ML) 为分析复杂的环境数据提供了潜力.
研究的目的:
- 评估ML模型在预测每日平均臭氧度方面的能力.
- 为臭氧预测确定相关的大气参数.
- 建立基于ML的空气质量预测的通用化方法.
主要方法:
- 分析来自瑞士国家空气污染监测网络 (NABEL) 的空气质量数据.
- 应用特征选择技术 (例如,最佳子集选择) 来确定预测参数.
- 开发和比较各种ML模型,包括人工神经网络,线性和非线性模型.
- 在卢加诺,杜本多夫和苏黎世的NABEL站使用2016-2023年的数据进行模型培训和验证.
主要成果:
- 一个具有12个组件的非线性ML模型,利用NO2,NOx,SO2,VOC,温度和辐射等参数,产生了最低的平均绝对误差 (MAE).
- 卢加诺预测的臭氧度达到了低至9μgm-3.3的MAE.
- 苏黎世和杜本多夫站的MAE分别为大约11μgm−3和13μgm−3.
- 最好的模型的准确性接近1μgm-3,低于观测的标准偏差.
结论:
- ML是分析复杂大气数据和预测臭氧度的宝贵工具.
- 简单的非线性ML模型可以同样有效,甚至比复杂的人工神经网络更有效.
- 开发的方法为将ML应用于空气质量监测和预测挑战提供了一个框架.
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