Prediction of PM2.5 Concentration in Lanzhou City Based on CNN-LSTM

Abstract

Accurate prediction of concentration can reduce the occurrence of many health problems and im-prove people’s quality of life. Most of the existing studies have established machine learning models or single deep learning models to predict concentration, and often only focus on the impact of single factor and ignore the comprehensive impact of multiple factors. Based on this, this paper adopted the method of combining one-dimensional convolution and long-term and short-term network (CNN-LSTM), using the pollutant concentration and meteorological data to predict the future short-term single-day concentration, and the results were compared with a single LSTM model. The results showed that the combined CNN-LSTM model has better prediction effect than the single LSTM model.