# Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model
## 1 Introduction  
With the deepening of urbanization, the improvement of productivity and the transformation of the production mode in China, the types of air pollutants have changed considerably. As particulate matter with a diameter less than or equal to 2.5 \(\upmu\)m in the air environment, PM2.5 causes not only serious harm to the human body, but also huge economic loss to society. PM2.5 is harmful to the human body mainly in the respiratory and cardiovascular systems; therefore, reducing PM2.5 concentrations can effectively reduce health risks [1]. Although China has improved remarkably in PM2.5 control in recent years, the task of emission reduction is still very difficult to carry out. Therefore, it is especially important to simulate and forecast the PM2.5 concentration, which is a positive guidance and reference for scientific decisions of industrial structures and the formulation of environmental pollution prevention and control measures. Statistical methods and numerical simulation methods can be used for pollutant concentration prediction. Potential forecasts are mainly based on meteorological conditions of atmospheric dilution and dispersion capacity [2]. Warnings are issued when weather conditions are expected to meet the criteria for potentially severe pollution. Concentration forecasts are direct predictions of pollutant concentrations in an area, and the predictions are quantitative. These air pollution prediction models can be classified as parametric and non-parametric or deterministic and non-deterministic. The prediction models used in this paper were parametric models, whose outputs are uncertain when the parameters of the equations in the model have to be determined. Previous research is of great help for the prediction of PM2.5 concentration. however, the methods used are not sufficient as they do not consider the characteristics of different periods and the future dynamic effects of the concentrations of PM2.5. Especially, if dramatic changes of meteorological factors occur, such as heavy rain or strong wind, most methods fail in the mining of the dynamic effect of meteorological factors on the concentrations of PM2.5. At the same time, prediction models fail to effectively simulate the PM2.5 concentration dependence of space and time. In view of the above limitations, this study proposes an LSTM network based on Copula entropy to accurately predict PM2.5 concentration. The contributions of this study mainly include three aspects:  
1. Copula entropy (CE) was used to measure the statistical independence between PM2.5 and meteorological factors and air pollutants. The non-linear correlation could be mined, and it will possible to better explore the dynamic influence of characteristic states at different times on PM2.5 concentration in the future to improve the accuracy of the prediction model. 2. This paper converted the correlation analysis results into the weights of each influencing factor, which were fed into the prediction model as input data for the prediction. This reflects the degree of influence of attribute characteristics on PM2.5 concentration change, thus improving the accuracy of the prediction model. 3. A genetic algorithm--a heuristic search algorithm--was adopted in this paper to determine the model parameters by optimization, which avoided repeated parameter tuning and further improved the accuracy of the model prediction. In this paper, CE was used to analyze the correlation between PM2.5 and meteorological factors and air pollutants and to select attributes. Meanwhile, the correlation analysis results were used as attribute weights and input data to predict PM2.5 pollutant concentration through the LSTM model. The organizational structure of this paper is as follows: the Section 2 introduces a background review of the theoretical basis, experimental theory and preliminary work; the Section 3 introduces the methodology applied in this paper; the Section 4 presents the experimental results of our work. Finally, we draw conclusions from the work of this paper in Section 5.