分析和预测首都和泰国南部的空气污染度,使用依赖滞后的高斯过程模型
Haris Khurram1, Apiradee Lim2,3
1Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani, 94000, Thailand. hariskhurram2@gmail.com.
这项研究比较了泰国南部和曼谷的空气污染,发现LDGP模型在预测方面优越. 这项研究有助于公共卫生规划和预防战略.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 空气污染对全球健康构成重大风险.
- 准确的空气污染预测对于公共卫生和环境管理至关重要.
研究的目的:
- 分析和比较泰国南部和曼谷的空气污染度.
- 开发和评估一种用于空气污染预测的新型机器学习模型.
主要方法:
- 提出了一个依赖滞后的高斯过程 (LDGP) 模型,并与AR-DNN,AR-RF,GB和KNN进行了比较.
- 分析了泰国南部和曼谷2018-2022年的每日空气污染数据.
- 十倍交叉验证和性能指标用于模型评估.
主要成果:
- 曼谷的整体空气污染度高于泰国南部.
- 泰国南部的PM,O3和CO污染物暴露率较高.
- 拟议的LDGP模型在预测所有空气污染物方面表现出卓越的性能.
结论:
- 建议使用LDGP模型进行空气污染预测和进一步研究.
- 调查结果可以为地方政府的公共卫生可持续性规划提供信息.
- 该方法在环境健康方面具有全球应用的潜力.
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