Evaluating deep learning time series models for PM2.5 forecasting across diverse horizons

iScience 2026 Open access

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Authors: Ling Zeng, Runan Dong, Meng Yuan, Linhai Jing, Shoutao Jiao
DOI: 10.1016/j.isci.2026.114770
Cited by: 3
References: 34

Abstract

and lower MAE% and RMSE%, especially when augmented by meteorological factors over pollutants; complete seasonal training improves performance, while gaps exceeding three months between training and prediction reduce reliability due to evolving PM2.5 dynamics. These findings underscore meteorological integration, data-driven modeling, seasonal completeness, and timely prediction for pollution control for policymakers in Chengdu.

Fields of Study & Subjects

Air Quality Monitoring and Forecasting Air Quality and Health Impacts Traffic Prediction and Management Techniques

Publication details

Journal / source
iScience
Publisher
Elsevier BV
Volume
29
Issue
2
Pages
114770–114770
Publication date
22 January 2026
Language
EN