使用交叉堆叠集体学习模型预测和解释臭氧变化.
Zhukai Ning1, Song Gao1, Zhan Gu2
1School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China.
The Science of the total environment
|May 22, 2024
概括
这项研究开发了一种机器学习模型,使用气象,污染物和前体数据预测每小时的臭氧度. 该模型实现了高精度,可以为减排战略提供信息.
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:
- 臭氧预测研究受益于增加对臭氧前体的监测数据.
- 机器学习为分析复杂环境数据集提供了先进的方法.
研究的目的:
- 开发和验证一种新的交叉堆叠集体学习模型 (CSEM),用于准确的小时臭氧度预测.
- 评估减排方案对臭氧水平的影响.
- 为预测模型提出一个全面的评估指数.
主要方法:
- 特性工程和重建多源数据,包括气象,常规污染物和挥发性有机化合物 (VOC) 数据.
- 开发一个集成随机森林,极端梯度增强树和长短期记忆 (LSTM) 基本模型的CSEM.
- 交叉堆叠的综合培训,以提高组合模型的性能.
主要成果:
- 对于轻度,中度和严重污染,CSEM的R2值分别为0.94,0.97和0.96,实现了高预测准确度.
- 平均绝对误差 (MAE) 为4.48 μg/m3,5.01 μg/m3和8.71 μg/m3对于各自的污染水平.
- 在NOx没有变化的情况下,VOC减少了20%,导致75.28%的病例中臭氧减少,而15.73%的病例中臭氧水平低于200μg/m3.
结论:
- 开发的CSEM在预测每小时臭氧度方面表现出卓越的性能.
- 该研究强调了机器学习在分析复杂的空气质量数据和为排放控制策略提供信息方面的有效性.
- 拟议的评估指数提供了一个标准化的方法来比较预测模型的性能.
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