在英格兰中部15个城市的每小时PM2.5趋势的趋势属性预测,应用优化的机器学习功能选择
1DWA Energy Limited, Lincoln, United Kingdom.
Journal of environmental management
|March 13, 2024
概括
这项研究使用机器学习预测每小时的颗粒物 (PM2.5),使用LASSO和支持矢量回归模型显示准确的短期预测 (0-3小时). 长期预测 (6-12小时) 需要进一步细化以提高准确性.
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:
- 从2018年到2022年,分析了英格兰中部15个城市的每小时颗粒物物质 (PM2.5) 趋势.
- 与2020-2022年相比,2018-2019年观察到较高的年平均PM2.5值和标准偏差.
- 城市背景和城市交通站点表现出一致的背景和峰值分布.
研究的目的:
- 为了证明从单变PM2.5数据的趋势属性的实用性,用于可靠的短期每小时预测.
- 为记录PM2.5趋势的区域差异提供洞察力.
- 开发和评估用于PM2.5预测的监督机器学习模型.
主要方法:
- 从前12小时的PM2.5数据中提取了15个趋势属性.
- 四个监督机器学习 (SML) 模型被用来预测PM2.5度,提前13小时.
- 采用了使用五个优化器和一个过拟合指示器的新功能选择程序.
主要成果:
- 拉索和支持向量回归模型获得了最佳性能,预测时间最长3小时 (t0-t+3) 的平均平均误差 (MAE) 为~1-3μg/m3.
- 这些模型没有显示过的迹象,正如一个基于交叉验证的指标所表明的那样.
- 未来6-12小时的预测显示MAE较高 (~3-4μg/m3),并倾向于低估极端PM2.5峰值.
结论:
- 提取的PM2.5趋势属性可以有效地用于短期每小时预测.
- 拉索和支向量回归为在不久的将来 (0-3小时) 提供可靠的PM2.5预测.
- 需要进一步改进模型,以提高PM2.5预测超过6小时的准确性.
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