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在德里提高颗粒物质预测:从统计和机器学习模型的见解

Divyansh Sharma¹, Sapan Thapar¹, Kamna Sachdeva²

¹Department of Sustainable Engineering, TERI School of Advanced Studies, New Delhi, India.

Environmental monitoring and assessment

|June 3, 2025

概括

这项研究使用时间序列和机器学习在德里模拟颗粒物 (PM10和PM2.5). 支持矢量机 (SVM) 模型准确预测了污染水平,为城市空气质量管理提供了见解.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
城市规划城市规划

背景情况:

颗粒物 (PM10和PM2.5) 在城市环境中对健康构成重大风险.
德里经历了由排放和天气影响的动态空气质量变化.
精确的PM水平建模对于有效的空气质量管理至关重要.

研究的目的:

在德里评估PM10和PM2.5预测的传统时间序列和机器学习模型.
建立空气质量变化的基线并确定趋势.
通过结合外源变量来提高预测准确性.

主要方法:

利用季节性分解进行基线空气质量分析.
采用时间序列模型 (ARIMAX,SARIMAX) 和机器学习模型 (RF,SVM).
包括外部变量,包括其他污染物和气象数据.

主要成果:

几个车站的PM10水平呈现上升趋势,而PM2.5水平整体下降.
支持矢量机 (SVM) 模型在预测颗粒物水平方面表现出卓越的准确性.
在SVM的测试中,PM10的RMSE达到12.48-67.22μg/m3,PM2.5.5的RMSE达到8.38-48.95μg/m3.

结论:

用外源因素增强的SVM模型为城市环境中PM预测提供了强大的方法.
结合各种外部变量可以显著提高环境建模和预测能力.
这项研究为决策者提供了可操作的见解,以推进城市空气质量管理.

关键词:

空气质量空气质量德里德里德里德里德里德里德里德里德机器学习是机器学习.颗粒物质颗粒物质是什么时间序列模型时间序列模型

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