一个基于机器学习的集合模型,用于估计台湾氧化度的日间变化
Aji Kusumaning Asri1, Hsiao-Yun Lee2, Yu-Ling Chen1
1Department of Geomatics, College of Engineering, National Cheng Kung University, Tainan, Taiwan.
The Science of the total environment
|January 26, 2024
概括
本研究使用机器学习组合模型估计了台湾的日间氧化 (NOx) 变化. 结果显示,白天NOx水平较高,特别是在北部和西部地区,有助于环境和健康规划.
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:
- 氧化 (NOx) 污染与自然和人为来源的人类活动有关.
- 评估NOx暴露的日间变化是一个重大的研究挑战.
- 了解时间和空间NOx模式对于有效的环境管理至关重要.
研究的目的:
- 通过使用27年的数据,估计台湾各地NOx度的日间变化.
- 开发和验证一种基于机器学习的新型组合模型,用于NOx暴露评估.
- 为政策制定提供有关氧化物污染的时间和空间模式的见解.
主要方法:
- 开发了一种机器学习集合模型,集成混合战争-土地使用回归 (LUR),机器学习算法和集合学习.
- 利用27年的数据来捕捉NOx的长期趋势和变化.
- 采用混合战争-LUR用于预测器选择和机器学习以提高性能.
主要成果:
- 整体模型在白天 (Adj-R2=0.93),夜间 (Adj-R2=0.98) 和每天 (Adj-R2=0.94) 的NOx估计中获得了很高的解释能力.
- 发现NOx度在白天明显高于夜间.
- 空间分析表明,在台湾北部和西部的NOx度最高.
结论:
- 开发的整体模型准确地估计了白天的NOx变化,优于仅依赖混合战争-LUR的模型.
- 这些发现强调了在NOx暴露评估中考虑白天模式的重要性.
- 这项研究为区域规划和排放控制战略提供了宝贵的参考资料,以改善环境和人类健康.
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