基于机器学习的框架,用于实时评估空气质量和预测环境健康风险映射
M Rajesh1, R Ganesh Babu2, Usha Moorthy3
1Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation (DU), Paiyanur, Tamilnadu, India.
Scientific reports
|August 6, 2025
概括
本研究介绍了使用机器学习的实时空气质量和健康风险预测系统. 它整合了各种数据来准确预测,并确定了脆弱人群,帮助及时做出公共卫生决策.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 城市和工业环境面临着严重的空气污染挑战,影响公众健康.
- 准确的,实时的空气质量评估对于有效的健康风险预测和缓解策略至关重要.
研究的目的:
- 为实时空气质量评估和健康风险预测开发和验证一个实用的,创新的框架.
- 整合多源数据和先进的机器学习,以便在各种环境中准确预测污染物.
主要方法:
- 使用了机器学习算法的组合,包括随机森林,梯度增强,XGBoost和长短期记忆 (LSTM) 网络.
- 来自固定/移动传感器,气象,卫星和人口来源的综合数据.
- 采用SHAP分析来确定模型的可解释性,并确定了关键的预测变量.
主要成果:
- 在预测污染物度和分类空气质量水平方面实现了高时间准确性.
- 开发了一个基于云的系统,通过Web仪表板和移动警报提供实时更新.
- 每五分钟生成视觉风险地图和健康咨询,识别脆弱人群.
结论:
- 该框架为实时空气质量监测和健康风险评估提供了一个强大的解决方案.
- 它增强了公共卫生官员和社区的决策能力.
- 未来的验证将侧重于现实世界的传感器部署和与健康影响记录的相关性.
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