使用气象数据进行PM2.5度预测的基于集成的分类方法
1Department of Computing Technologies, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India.
Frontiers in big data
|June 26, 2023
概括
机器学习准确地分类了空气污染 (PM2.5) 的水平. 随机森林与SMOTE平衡提高了预测空气质量指数类别的准确性,增强了公共卫生洞察力.
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:
- 作为全球健康威胁的空气污染,使用空气质量指数 (AQI) 进行监测.
- 准确的AQI预测和分类对于公共卫生至关重要.
- 监督机器学习为分析空气质量数据提供了先进的方法.
研究的目的:
- 使用机器学习将颗粒物 (PM2.5) 度分为不同的类别.
- 评估和比较用于PM2.5分类的各种机器学习算法的性能.
- 解决空气质量分析中数据集不平衡所带来的挑战.
主要方法:
- 实现多个机器学习算法:逻辑回归,支持向量机,随机森林,极端梯度增强和多层感知.
- 网格搜索的应用用于对分类器的超参数优化.
- 使用基于SMOTE的方法来平衡不平衡的数据集,以改善模型培训.
主要成果:
- 与原始数据集上的其他方法相比,随机森林多类分类器显示出更高的准确性.
- 基于SMOTE的数据集平衡显著提高了随机森林分类器的性能.
- 准确性和每类准确性是比较算法有效性的关键指标.
结论:
- 机器学习,特别是带有SMOTE平衡的随机森林,为分类空气污染水平 (PM2.5) 提供了有效的方法.
- 对空气质量数据的准确分类可以支持更好的公共卫生战略.
- 解决数据不平衡对于可靠的空气质量预测模型至关重要.
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