使用亚集合回归模型和机器学习算法在亚洲大城市,Dhaka,孟加拉国估计地面PM2.5
Abu Reza Md Towfiqul Islam1, Mohammed Al Awadh2, Javed Mallick3
1Department of Disaster Management, Begum Rokeya University, Rangpur, Rangpur, 5400 Bangladesh.
Air quality, atmosphere, & health
|June 12, 2023
概括
这项研究使用先进的机器学习提高了达卡的细颗粒物 (PM2.5) 预测. 随机子空间模型为PM2.5度提供了最准确的估计.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 大气化学 大气化学
背景情况:
- 微细颗粒物 (PM2.5) 构成严重的健康和环境风险,由城市化和工业化驱动.
- 传统的统计模型在准确预测PM2.5度方面存在局限性.
- 机器学习提供了卓越的预测能力,但对各种方法的研究是有限的.
研究的目的:
- 通过使用各种统计和机器学习模型,估计达卡的地面PM2.5度.
- 分析气象因素和空气污染物对PM2.5动态的影响.
- 为准确的PM2.5预测确定最佳模型.
主要方法:
- 采用最佳子集回归和机器学习算法:随机树,增量回归,减少错误修剪树和随机子空间.
- 使用了2012-2020年的数据,包括气象变量和污染物 (NOx,SO2,CO,O3).
- 使用统计错误指标评估模型性能.
主要成果:
- 最好的子集回归有效地预测了使用降水,湿度,温度,风速,SO2,NOx和O3的PM2.5.
- 降水量,相对湿度和温度显示与PM2.5.5有负相关性.
- 随机子空间模型在最小的错误指标下表现出卓越的性能.
结论:
- 组合学习模型被推用于PM2.5度估计.
- 这些发现将有助于量化PM2.5暴露,并为区域污染控制战略提供信息.
- 准确的PM2.5监测对于公共卫生和环境保护至关重要.
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