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基于二次分解的AQI预测的混合多尺度融合范式

Yufan Deng¹, Tianqi Xu¹, Zuoren Sun^2,3

¹School of Business, Shandong University, Weihai, 264209, People's Republic of China.

Environmental science and pollution research international

|April 24, 2024

概括

这项研究引入了一种新的混合模型,用于准确预测空气质量指数 (AQI). 这种先进的方法提高了预测准确度,这对公共卫生和环境政策至关重要.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
时间序列分析时间序列分析

背景情况:

快速的工业化和城市化加剧了空气污染.
准确的空气质量指数 (AQI) 预测对于公共卫生和政策至关重要.
现有的预测模型与AQI时间序列的复杂性作斗争.

研究的目的:

为复杂的AQI时间序列提出混合多尺度聚变预测范式.
提高AQI预测的准确性和稳定性.
为AQI控制策略提供有价值的参考资料.

主要方法:

使用完整的集体实证模式分解与自适应噪声 (CEEMDAN) 和样本 (SE) 进行初始数据处理.
利用变化模式分解 (VMD) 和K-平均集群用于二次分解和高频数据的集成.
使用长短期记忆 (LSTM) 网络实施多规模的融合培训.

主要成果:

实现了高预测准确度,R2为0.9715,RMSE为2.0327,MAE为0.0154和MAPE为0.0488. 这两种方法都具有很高的预测准确度.
在四个不同城市的AQI数据集中展示了卓越的稳定性和通用性.
在AQI时间序列预测中表现优于基线方法.

结论:

拟议的混合多尺度融合范式在AQI预测准确性方面提供了显著的改进.
该模型显示了在开发有效的AQI预测系统方面具有强大的实际应用潜力.
这项研究为未来关于空气质量管理和控制策略的研究提供了宝贵的参考.

关键词:

空气质量指数预测预测 K-表示集群.长期短期内存网络中的长期内存.多尺度的核聚变技术二次分解二次分解

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