Prediction of PM2.5 Concentration Based on CEEMD-LSTM Model

At present, there are serious air pollution problems in most cities in China. As one of the main atmospheric pollutants, PM_2.5 has caused serious harm to people's health. In order to improve the accuracy of PM_2.5 concentration prediction, this paper proposes a new hybrid model based on complementary ensemble empirical mode decomposition (CEEMD) and Long Short-Term Memory (LSTM) to predict daily PM_2.5 concentration. The daily PM_2.5 concentration and meteorological data from January 2010 to December 2014 released by the US Embassy are selected as experimental data. Compared with extreme learning machine (ELM), Support Vector Regression (SVR) and Long Short-Term Memory (LSTM), the CEEMD-LSTM model shows a higher prediction ability.