不健康空气污染事件的严重程度等级的概率分类
Nurulkamal Masseran1, Muhammad Aslam Mohd Safari2, Razik Ridzuan Mohd Tajuddin3
1Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia. kamalmsn@ukm.edu.my.
本研究引入了一种机器学习方法,将空气污染事件分类为极端或非极端. 天真贝叶斯模型准确地预测了这些事件,有助于环境监测和公共卫生政策.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 空气污染事件带来了重大的环境和公共卫生风险.
- 区分极端和非极端污染事件对于有效管理至关重要.
- 目前的方法可能缺乏及时准确分类所需的精度.
研究的目的:
- 开发和评估一种概率机器学习技术,用于对空气污染事件进行分类.
- 评估天真贝叶斯模型在预测极端和非极端空气污染方面的有效性.
- 为公共卫生和决策当局提供可靠的工具.
主要方法:
- 使用了概率机器学习技术,特别是天真贝叶斯模型.
- 将模型应用于马来西亚Klang (1997-2020) 的空气污染指数数据.
- 在训练和测试数据集上使用准确度,灵敏度和特异性来评估模型性能.
主要成果:
- 天真贝叶斯模型表现出高精度,灵敏度和特异性.
- 该模型有效地将空气污染事件分为极端和非极端类别.
- 该研究证实了该模型适用于真实世界空气污染分析的适用性.
结论:
- 天真贝叶斯模型为预测空气污染事件的严重程度提供了准确而高效的解决方案.
- 这种方法为环境监测和可持续空气质量管理提供了有价值的数据.
- 调查结果支持公共卫生和政策倡议的知情决策.
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