Air quality prediction model based on deep learning hybrid framework

Abstract

As modernization and industrialization continue to accelerate, air pollution has become an increasingly pressing problem. Air quality prediction is considered an essential technical support for air pollution prevention and control. To achieve more accurate predictions for urban air pollution, we propose a hybrid model called CBLA, which consists of three parts: one-dimensional Convolutional Neural Networks (1D-CNNs), Bidirectional Long Short-Term Memory network (BiLSTM), and attention mechanism. Firstly, 1D-CNNs extract the deep features of the original data. Secondly, BiLSTM mines time-series features for initial prediction. Finally, the attention mechanism captures the effect of characteristic conditions on PM2.5 concentration at different times to further optimize the model. The eXtreme Gradient Boosting (XGBoosting) tree is used to integrate the preliminary prediction results and meteorological data to improve prediction accuracy further. We conducted extensive experimental evaluations using Beijing's air quality and meteorological datasets, which showed that the CBLA model has excellent performance and model expression power.