A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration

Abstract

Urban air pollutant concentration prediction is dealing with a surge of massive environmental monitoring data and complex changes in air pollutants. This requires effective prediction methods to improve prediction accuracy and to prevent serious pollution incidents, thereby enhancing environmental management decision-making capacity. In this paper, a new pollutant concentration prediction method is proposed based on the vast amounts of environmental data and deep learning techniques. The proposed method integrates big data by using two kinds of deep networks. This method is based on the design that uses a convolutional neural network as the base layer, automatically extracting features of input data. A long short-term memory network is used for the output layer to consider the time dependence of pollutants. Our model consists of these two deep networks. With performance optimization, the model can predict future particulate matter (PM2.5) concentrations as a time series. Finally, the prediction results are compared with the results of numerical models. The applicability and advantages of the model are also analyzed. The experimental results show that it improves prediction performance compared with classic models.