Hybrid framework for improved PM2.5 prediction based on seasonal-trend decomposition and tailored component processing

Hybrid framework for improved PM2.5 prediction based on seasonal-trend decomposition and tailored component processing

Abstract Precise prediction of PM2.5 concentrations is crucial for effective environmental management and the safeguarding of public health. However, accurately forecasting PM2.5 levels presents significant challenges due to the complex, non-linear, and non-stationary characteristics inherent in the...

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Bibliographic Details
Main Authors: Dongbao Jia, Wenjun Ruan, Rui Ma, Shiwei Zhao, Yichen Wang, Wei Xu, Weijie Zhou, Xinxin Ge, Zhongxun Xu
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Air quality management
Time series prediction
Data preprocessing technique
Advanced hybrid model
Online Access:https://doi.org/10.1038/s41598-025-04597-x
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