使用机器学习技术预测空气质量指数和空气污染
Mostafa M Abdelmalek1,2, Hatem Mahmoud3,4, Hassan Shokry5
1Environmental Engineering Department, Egypt-Japan University of Science and Technology, Alexandria, 21934, Egypt. mostafa.abdelmalek@ejust.edu.eg.
Scientific reports
|July 16, 2025
概括
机器学习模型准确地预测空气质量. 高斯过程回归 (GPR) 实现了超过96%的准确性,优于其他有效的空气污染监测和控制策略的模型.
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:
- 由PM2.5,PM10,O3,NO2,SO2和CO等污染物造成的空气污染对公众健康和环境可持续性构成重大风险.
- 准确的空气质量监测和预测对于实施有效的控制策略和维护福祉至关重要.
研究的目的:
- 开发和比较五种机器学习 (ML) 模型,用于预测空气质量指数 (AQI).
- 评估使用减少的一组关键污染物 (PM2.5,PM10,CO) 用于AQI预测的有效性,优化测量和分析效率.
主要方法:
- 利用了2022年的历史空气污染数据集,包括六种污染物.
- 应用随机森林识别特征的重要性,选择PM2.5,PM10和CO用于AQI预测.
- 开发并比较了高斯过程回归 (GPR),集合回归 (ER),支向量机 (SVM),回归树 (RT) 和内核近似回归 (KAR) 模型.
主要成果:
- GPR,ER,SVM和RT模型在AQI预测中实现了超过96%的准确性.
- 卡尔模型的准确性较低,为82.36%.
- GPR模型表现出卓越的性能,最低的根平均平方误差 (RMSE) 为0.87 (训练) 和1.219 (测试).
结论:
- 机器学习模型,特别是GPR,为空气质量预测和监测提供了高度准确和高效的方法.
- 这项研究验证了为简化AQI评估而使用有限的一组重要污染物的有效性.
- 这些发现支持开发用于实时空气质量分析的先进工具,有助于制定有效的污染控制战略,促进公共卫生和环境可持续性.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.3K
09:33Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
Published on: December 23, 2022
2.4K
相关概念视频
01:18Steps in Outbreak Investigation
209
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
209
01:25Statistical Methods for Analyzing Epidemiological Data
540
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
540