Prediction of Air Quality in the Beijing-Tianjin-Hebei Region Based on LSTM Model

Abstract

The air environment plays a vital role in human life and is closely related to the soundness of the ecosystem and the safety of human life, and good air quality is one of the prerequisites for the sustainable development of cities and society. In this paper, the Beijing-Tianjin-Hebei region is selected as the research object to explore the regional air quality characteristics, predict air quality changes, and seek scientific and effective methods and suggestions to improve air quality. In this paper, an air quality prediction model based on the long- and Long Short-Term Memory Networks (LSTM) is established by using the daily average AQI data of six cities in the Beijing-Tianjin-Hebei region for a total of 1,953 days from January 1, 2018, to April 30, 2023, respectively. Finally, the established model was evaluated using several evaluation metrics such as root mean square error (RMSE). The results show that the LSTM-based neural network can predict the AQI more accurately, which provides a scientific and reasonable theoretical basis and prediction method for the environmental protection and related decision-making of governmental departments.