适应性功能分量订单加权平均方法及其应用于污染物度分析的应用
Yang Li1, Xiaoxue Hu1, Maozai Tian2
1School of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi, Xinjiang, China.
PloS one
|February 13, 2026
概括
一种新的自适应性函数式量级加权平均 (FP-OWA) 方法改善了复杂的环境数据的排名. 这种方法通过揭示污染模式来提高空气质量管理,以便制定更好的污染控制策略.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 统计建模 统计建模
背景情况:
- 评估不断变化的污染物度对于环境政策至关重要.
- 空气质量管理需要科学合理的多标准排名方法.
- 现有的方法需要加强复杂的功能数据分析.
研究的目的:
- 提出一种新的自适应性函数式断片顺序加权平均 (FP-OWA) 方法.
- 为了提高环境应用的复杂功能数据的排名.
- 为区域污染评估和控制提供一个强大的工具.
主要方法:
- 开发了一种自适应的函数式断片顺序加权平均 (FP-OWA) 方法.
- 集成数据平滑,基于深度的中心性和基于等级的聚合.
- 进行蒙特卡洛模拟,将FP-OWA与现有方法进行比较.
主要成果:
- FP-OWA表现出更好的排名一致性和稳定性,特别是在有噪音数据的情况下.
- 该方法准确地揭示了北京-天津-河北地区的PM2.5和O3的时空污染模式.
- FP-OWA为污染控制策略提供了可靠的技术基础.
结论:
- 新的FP-OWA方法为环境研究中的功能数据排名提供了显著的改进.
- 精确评估区域污染模式,支持有效的空气质量管理.
- 未来的工作重点是扩展FP-OWA用于复杂数据和大数据处理.
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