Air quality index forecast in Beijing based on CNN-LSTM multi-model

Accurate predicting the air quality trend can provide a theoretical basis for environmental protection management and decision-making. This study proposed the convolutional neural networks-long short-term memory (CNN-LSTM) model, which was proposed to improve the air quality prediction accuracy. Firstly, CNN's efficient feature extraction function was used to extract data features. Then the feature vectors were constructed into the sequence form, which was transmitted to the LSTM network. The LSTM layer learned the changing rules of air quality data to predict future data. Taking Beijing's air quality index as an example, the prediction results of the CNN-LSTM model were compared with those of auto-regressive moving average (ARMA), seasonal auto-regression integrated moving average (SARIMA), recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) models. The results show that, compared with other single prediction models, the CNN-LSTM achieved the highest prediction accuracy. In particular, CNN-LSTM was compared with the SARIMA model, which is a time series representative model. The indicators of the CNN-LSTM model have been well improved. The mean absolute error (MAE) and root mean square error (RMSE) of the CNN-LSTM were reduced respectively 3.17% and 5.46%, and R2 was improved 8.45%.