Prediction of PM2.5 concentration in urban agglomeration of China by hybrid network model

Abstract

The urban agglomeration area is a heavy disaster area of PM2.5 pollution, and the problem of PM2.5 pollution seriously affects the natural environment and public health. Accurate prediction of PM2.5 concentrations in urban agglomerations is the basis for effective environmental management. Deep learning methods such as Bi-directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) have been applied to PM2.5 concentration prediction in urban agglomerations. However, the existing research methods have low prediction accuracy and poor practicality, and lack a universal method to accurately predict PM2.5 concentrations in 19 urban agglomerations of China. Based on this, this paper combines BiLSTM and CNN, and introduces a Learning Rate Schedule (LRS) to propose a hybrid model of LRS + BiLSTM + CNN. This model combines the advantages of BiLSTM and CNN to extract temporal and spatial features of PM2.5 data. In addition, LRS is introduced in the process of model training, which dynamically adjusts the learning rate of the network model and reduces the training cost of the model. Finally, the daily average datasets of six air qualities from nineteen urban agglomerations of China are used as examples, and seven benchmark network models, CNN, Long Short-Term Memory, BiLSTM, CNN + LSTM, LSTM + CNN, and CNN + BiLSTM, are considered for validation experiments. The results show that the prediction effect of the proposed model is significantly better than that of the benchmark model in 17 urban agglomerations, such as Mid-southern Liaoning urban agglomerations and Central Yunnan urban agglomeration. Compared with the above seven benchmark models, the prediction accuracy is improved by 12.7%, 8.9%, 12.4%, 12.8%, 16.4%, 19.7%, 9.5%; 10.2%, 24.2%, 14.4%, 17.9%, 18.4%, 19.9% and 2.6% respectively. The LRS is fully applicable to nineteen urban agglomerations, which has clear effects on the optimization of the network model. LRS + BiLSTM + CNN has high accuracy and practicality in predicting PM2.5 concentrations in urban agglomeration areas in China, and can provide effective technical support for environmental governance of urban agglomeration areas.