Abstract
Long-term prediction of hour-concentration of PM2.5 (particles in atmospheric suspension with effective dimensions equal or lower than 2.5 microns) is of great significance for environmental protection and people’s health. At present, the prediction of hour-concentration of PM2.5 is mostly single-step prediction, which is to predict PM2.5 concentration at a future time point based on a period of historical data. In this paper, a model based on multi-time scale fusion is proposed to study single-step prediction and multi-step prediction, respectively. Experimental results show that the proposed model is better than stacked LSTM and CNN-LSTM in predicting PM2.5 hour-concentration.