Analysis and prediction of air quality in Xiangyang City

Xiangyang, as an important city in central China, is faced with the increasingly serious air pollution problem during the development of economy and urbanization, which seriously affects the health of residents and the sustainable development of the city. Therefore, it is of great significance to analyze and forecast the air quality of Xiangyang City. This paper will explore the degree of influence of different pollutants on AQI through correlation analysis, and train and forecast the air quality data of Xiangyang City by constructing random forest model, BP neural network model and LSTM model. Finally, by comparing the root mean square error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of different models on the test set, the results show that all indexes of the random forest model are lower than the BP neural network model and the LSTM model, that is, the random forest model is the most suitable for the prediction of Xiangyang air quality