A Deep Learning Model Integrating EEMD and GRU for Air Quality Index Forecasting

Accurate prediction of the air quality index (AQI) is essential for environmental monitoring and sustainable urban planning. With rising pollution from industrialization and urbanization, particularly from fine particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), and ozone (O3), robust forecasting tools are needed to support timely public health interventions. This study proposes a hybrid deep learning framework that combines empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) with two recurrent neural network architectures: long short-term memory (LSTM) and gated recurrent unit (GRU). A comprehensive dataset from Xitun District, Taichung City—including AQI and 18 pollutant and meteorological variables—was used to train and evaluate the models. Model performance was assessed using root mean square error, mean absolute error, mean absolute percentage error, and the coefficient of determination. Both LSTM and GRU models effectively capture the temporal patterns of air quality data, outperforming traditional methods. Among all configurations, the EEMD-GRU model delivered the highest prediction accuracy, demonstrating strong capability in modeling high-dimensional and nonlinear environmental data. Furthermore, the incorporation of decomposition techniques significantly reduced prediction error across all models. These findings highlight the effectiveness of hybrid deep learning approaches for modeling complex environmental time series. The results further demonstrate their practical value in air quality management and early-warning systems.