一个新的混合优化模型用于评估和预测空气质量等级.
Yumei Shi1, Sheng Wang2, Xiaomei Yu1
1School of Mathematics and Finance, Chuzhou University, 1 HuifengRoad, Chuzhou, 239000, Anhui, China.
Environmental monitoring and assessment
|August 9, 2024
概括
这项研究介绍了FP-RF,这是一种通过聚集城市和分析污染成分来准确预测空气污染的新方法. 该模型在预测污染等级和城市级空气质量方面取得了很高的准确性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 空气污染对自然资源和公共健康构成重大全球风险.
- 准确的空气污染预测对于实施有效的控制策略至关重要.
- 主要污染物的区域差异对城市特定预测提出了挑战.
研究的目的:
- 为准确的空气污染预测开发一种新的方法,以适应区域差异.
- 根据空气质量指数 (AQI) 值对目标分析进行城市分类.
- 为了提高空气质量等级预测在多个城市的准确性.
主要方法:
- 集群算法的集成以通过AQI将城市分组.
- 应用功能主要成分分析 (FPCA) 来识别集群中的关键污染成分.
- 使用增强的随机森林算法来预测空气质量等级.
主要成果:
- 在实验评估中,FP-RF模型表现出高效率.
- 在预测六种不同的污染等级时,平均准确率为98.6%.
- 在预测16个县级城市的空气质量方面达到96.04%的准确性.
- 在预测准确性方面表现优于现有的基线模型.
结论:
- 拟议的FP-RF方法为空气污染预测提供了强大而准确的方法.
- 集群化和FPCA有效地解决了空气污染的区域差异.
- 该模型为公共卫生和环境管理提供了有价值的工具.
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