通过机器学习和基于游戏理论的方法,估计影响曼谷机场可见性和理解影响其变化的因素
Nishit Aman1, Sirima Panyametheekul2,3,4, Sumridh Sudhibrabha5
1Department of Environmental and Sustainable Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.
Environmental science and pollution research international
|August 5, 2024
概括
机器学习模型准确地预测曼谷机场的能见度,确定空气污染物和湿度是关键因素. 这项研究突出了气溶湿透增长对可见性降低的影响.
科学领域:
- 环境科学 环境科学
- 大气科学 大气科学
- 数据科学数据科学数据科学
背景情况:
- 可见性对于航空安全和空气质量评估至关重要.
- 了解影响可见性的因素对于环境管理至关重要.
- 之前的研究已经探索了各种可见度预测方法.
研究的目的:
- 用机器学习模型估计曼谷机场白天可见度.
- 识别和分析影响可见性的关键因素.
- 评估单个和整体机器学习模型的性能.
主要方法:
- 使用了六个单独的机器学习模型 (随机森林,AdaBoost,梯度提升,XGBoost,LightGBM,CatBoost) 和一个堆叠组合模型 (SEM).
- 采用沙普利增量解释 (SHAP) 方法来解释模型预测和识别有影响力的变量.
- 分析了预测变量,包括空气污染物 (PM2.5,PM10,O3),气象因素 (RH) 和与时间相关的变量 (朱利安日).
主要成果:
- 轻度梯度增强机 (LGBM) 模型在每小时和每天的尺度上展示了个别模型中最好的性能.
- 堆叠组合模型 (SEM) 的表现优于单个模型,特别是在日常规模上.
- 相对湿度 (RH),PM2.5,PM10,朱利安日 (JD) 和臭氧 (O3) 被确定为影响可见性的最重要因素.
- 超过某个值,观察到可见度和RH之间的负相关性,这归因于气溶的湿透增长.
结论:
- 机器学习技术对于预测可见性和了解其影响因素是有效的.
- 气溶的湿透生长显著影响可见性,特别是在较高的相对湿度水平.
- 该研究为该地区的空气质量管理和航空安全提供了宝贵的见解.
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