PM2.5 Concentration Prediction Using CNN-LSTM Model Based on Multi-Feature Fusion

Abstract

In order to solve the problem that the existing PM_2.5 concentration prediction methods ignore the spatial and temporal influencing factors of PM_2.5 concentration, this paper constructs a spatial characteristic factor of PM_2.5 concentration based on the maximum information coefficient, and proposes a CNN-LSTM combined prediction model based on multi-feature fusion, which transforms the abstract spatial and temporal influencing factors into quantifiable features. The model has good feature extraction ability and strong ability to capture short-term transient information and long-range dependent information in time series data, which improves the prediction performance of the model. The experimental results show that the prediction accuracy of CNN-LSTM model based on multi-feature fusion is 87.21%, and MAPE is 6.25, 4.84, and 1.29 less than BP, SVR, and LightGBM, and 1.91 and 7.04 less than CNN and LSTM.