Air Pollutants NO$$_{2}$$ Concentration Prediction Based on LSTM Neural Network method

Abstract

In recent years, the China’s economy has developed rapidly. The people’s living standard has been improved. The number of cars has been increasing, and the pollutant NO has been produced continuously, which leads to the formation of NO\(_{2}\). These harmful particles have an impact on human health. Thus, the effective and accurate NO\(_{2}\) concentration prediction model plays an effective role in people’s health and prevention. For this problem, this paper presents a prediction model based on the long short-term memory (LSTM) method to predict NO\(_{2}\) concentration. Firstly, the PM\(_{10}\), SO\(_{2}\), NO\(_{2}\), CO, O\(_{3}\), temperature in a campus monitoring point in Beijing is collected as the research object in this paper. Then, the LSTM prediction model and BP (back propagation) neural network prediction model are established respectively. Finally, the accuracy of the two prediction models for the prediction of NO\(_{2}\) concentration is compared. The results show that the prediction model based on LSTM method is superior to BP neural network model, and the prediction accuracy is more accurate.