PREDICTION OF AIR QUALITY INDEX BASED ON ARIMA AND LSTM MODELS—TAKE GUILIN AS AN EXAMPLE

With the acceleration of urbanization and the improvement of industrialization, air quality has become an important factor affecting the quality of life and health of urban residents. Under this background, the air quality problem has become increasingly prominent and has become a key factor affecting the quality of life and health of urban residents. Therefore, how to accurately predict the air quality index (AQI) in order to take timely and effective measures to protect the health of residents has become a major challenge we face. This study takes Guilin city as the object, and constructs an air quality prediction model by integrating big data and artificial intelligence technology. Based on the historical monitoring data such as PM2.5 and NO2, a training set/test set division method is adopted, and the ARIMA time series model and the LSTM depth learning network are comprehensively applied: the former captures the trend and seasonal characteristics of the data, and the latter handles the complex sequence relationship. By introducing the rolling prediction mechanism to update the model parameters in real time, the dynamic adaptability of the prediction system is effectively improved. Experiments show that the hybrid model significantly improves the prediction accuracy and stability of AQI, and provides a scalable technical scheme for urban air quality management.