使用自动机器学习的城市臭氧变化:从不同的特征重要性方案推断
Sankar Jyoti Nath1, Imran A Girach2, S Harithasree3,4
1Centre for Environment and Energy Development, Ranchi, 834001, India.
Environmental monitoring and assessment
|March 23, 2024
概括
机器学习准确地模拟了印度城市的地面层臭氧污染,识别了推动其变化的关键因素. 这种方法有助于理解和预测空气质量对人类健康和植被的影响.
科学领域:
- 大气化学 大气化学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 地表臭氧是一种有害的空气污染物和温室气体,城市排放加剧了污染.
- 有限的观测数据和不确定的驱动因素阻碍了发展中国家对臭氧变异性的理解.
研究的目的:
- 使用机器学习 (ML) 模拟臭氧变化,并确定印度主要城市环境中的关键影响因素.
- 评估自动化ML (AutoML) 在模拟每日臭氧度方面的有效性.
主要方法:
- 使用的ML模型包括来自科珀尼克斯大气监测服务 (CAMS) 的臭氧前体 (NO2,NO,CO,C5H8,CH2O) 和来自ERA5.5的气象数据.
- 使用自动化ML (AutoML) 来优化日常臭氧模拟的深度学习模型.
- 应用特征重要性方案 (SAGE,换重要性) 来理解模型驱动器.
主要成果:
- 配备了AutoML的深度学习模型模拟了每日臭氧,根平均平方误差 (RMSE) 为2ppbv,捕获了84-88%的变化.
- 模型性能与随机森林 (RF) 和XGBoost模型相比较.
- 城市臭氧模拟实现了2.5ppbv的RMSE和0.78的R2,使用不同重要性方案确定的前四个特征.
结论:
- ML,特别是AutoML,为模拟和理解城市臭氧变异提供了强大的工具,补充了传统模型.
- 从多个方案中对特征重要性进行科学分析对于推断臭氧光化学中的变量作用至关重要.
- 这种方法可以提高城市臭氧模拟和预测的准确性,这对于空气质量管理至关重要.
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