Research On PM_2.5 Concentration Prediction Based On Principal Component Analysis And Neural Network

With the rapid development of urbanization and industrialization,the fuel is continuously consumed and the atmospheric pollution is becoming more and more serious.The frequent haze has seriously affected people's normal work,life and health,and has become the focus of national attention,urgently need to be solved.An important factor in this phenomenon is the presence of fine particulate PM_2.5 in the air.Therefore,it is imperative to monitor the concentration of PM_2.5 suspended in air,and then make effective prediction according to the monitoring data.In this paper,the temporal variation of PM_2.5 is analyzed based on historical monitoring data,and from the perspective of air pollutants,meteorological factors and heating,the key factors influencing fine PM_2.5 were explored.Based on the relationship between PM_2.5 and other pollutants,stepwise regression analysis was used to analyze the causes of PM_2.5 pollution,the optimal model of stepwise regression was obtained,the evolution rule of secondary particles was described.In order to achieve the fine particles in the atmosphere prediction of PM_2.5 pollution better,this paper combines the method of principal component analysis and BP neural network theory,a neural network model is established by using MATLAB software,the simulation result is obtained.Finally,so as to judge the prediction ability,the prediction results of PCA-BP neural network combined with principal component analysis and neural network are compared with those of traditional BP neural network.PM_2.5 concentration prediction is only applicable to the short-term forecast,due to many factors that affect it,and easily affected by the surroundings.In this paper,the PCA-BP neural network theory is applied to predict the concentration of suspended particulate matter in the atmosphere for the first time.The aim of the study is to analyze PM_2.5 pollution characteristics and future short-term changes,provide some reference for the analysis of the environmental pollution.