A Novel Framework for Air QualityForecasting Using Graph ConvolutionalNetwork-Based Time Series Decomposition

作者：Huimin Han, Chan-Su Lee, Muhammad Tahir Naseem, Mughair Aslam Bhatti, Nadia Sarhan, Emad Mahrous Awwad, Yazeed Yasin Ghadi · 发表于：Polish Journal of Environmental Studies · 年份：2024 · DOI：10.15244/pjoes/187607 · 被引用次数：1 · 研究领域：Time Series Analysis and Forecasting、Neural Networks and Applications、Metaheuristic Optimization Algorithms Research

This study provides an empirical investigation into the effectiveness of several deep learning models in forecasting ambient concentrations of particulate matter with a diameter of less than 2.5 micrometers (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2). These pollutants are critical due to their adverse impacts on human health and the environment. We evaluated four distinct models: Graph Convolutional Network (GCN), Empirical Mode Decomposition combined with GCN (EMD+GCN), Ensemble Empirical Mode Decomposition with Gated Recurrent Unit and GCN, and GCN with an attention mechanism (GCN_ATT). Through rigorous computational experiments, the models were assessed against multiple statistical metrics including Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R2). The EEMD+GRU+GCN model consistently outperformed the others across all pollutants, demonstrating the lowest MAE, indicating its strong predictive accuracy. Similarly, it maintained the smallest MSE, suggesting it was particularly adept at reducing the influence of larger errors in predictions. Moreover, it achieved the lowest MAPE across the datasets, confirming its robustness in percentage terms relative to the scale of the actual values, a critical indicator of practical applicability for air quality forecasting. The GCN model, while founda...