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基于高频和低频IMF与EMD分解的PM_2.5度的混合预测模型

Ping Wang¹, Qingdong Wu², Guisheng Zhang³

¹College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan, 030006, China. 20181006@sxufe.edu.cn.

Scientific reports

|January 10, 2026

概括

一个新的混合模型将经验模式分解 (EMD) 与长期短期记忆 (LSTM) 和自行回归集成移动平均值 (ARIMA) 结合起来,显著改善了每小时预测颗粒物 (PM2.5) 度.

科学领域:

环境科学与工程环境科学与工程
大气科学大气科学
数据科学和机器学习

背景情况:

北中国的重工业造成严重的空气污染,颗粒物 (PM2.5) 是造成雾的主要污染物.
高度的PM2.5严重扰乱日常生活和工业活动,需要准确的预测以减轻影响.
在全国范围内有效的空气污染控制依赖于强大的区域预测模型,特别是像中国北部这样的地区.

研究的目的:

在中国北方六个主要城市开发和验证PM2.5度的高级每小时预测模型.
通过利用PM2.5时间序列数据的时间频率特征来提高预测准确度.
为该地区的空气污染预防和控制战略提供一个实际的工具.

主要方法:

利用了北京,天津,石家庄,太原,济南和州的每小时PM2.5数据.
使用实证模式分解 (EMD) 来将非线性,非静止的PM2.5数据分解为内在模式函数 (IMF) 和余量.
集成长期短期内存 (LSTM) 用于高频次序预测,自行回归集成移动平均 (ARIMA) 用于低频次序预测,支持矢量机 (SVM) 用于最终结果集成 (混合EMD_HL模型).

主要成果:

与单个预测模型相比,混合EMD_HL模型显著提高了PM2.5度预测的准确性.
该模型在多个实验中实现了大于0.69的方向指示器 (DA),表明了卓越的方向预测能力.
混合方法有效地挖掘数据信息并捕捉固有的时间序列特征,增强模型适应性.

结论:

拟议的混合EMD_HL模型为北中国的每小时PM2.5预测提供了实质性的进步.
该模型的增强预测准确性和定向性对于有效的空气质量管理和公共卫生保护至关重要.
这项研究为预测复杂的城市环境中的空气污染物提供了有价值的方法.

关键词:

自动回归的集成移动平均线经验模式分解分解长期短期内存网络内存长期短期内存预测PM2.5度的时间支持矢量机器的支持矢量机器.

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