Hybrid Prediction Model Of Air Quality Index Based On Fractal Interpolation Theory

Since the Second Industrial Revolution,the industrialized society has continued to develop on a global scale.While advanced industries have raised the level of productivity of the entire society and improved the overall living conditions of the people,they have also brought many environmental problems that cannot be ignored.Large and small factories are built,During the production process,many pollutants enter the natural environment.With the popularization of family cars,the problem of car exhaust emissions has gradually increased.At the same time,the concept of low-carbon life and green travel is still It has not become a social consensus,and with the increasing negative impact of air pollution on human society,the air quality index has become one of the important contents of people’s attention in daily weather forecasts.It not only affects people’s production and life,but also affects people’s production and life.To protect everyone’s health.Therefore,the importance of the relevant air quality index prediction research is self-evident.Historically,the predictive research methods of air quality index are divided into three categories: first,time series analysis;Time series analysis analyzes the characteristics of AQI over time,regression analysis establishes a relationship between AQI and influence factors for quantitative analysis,and neural network analysis takes relevant data as input values,builds network topology,takes AQI as a prediction object,and approaches the reality.This paper uses time series analysis theory to analyze the fractal characteristics of air quality index time series from the perspective of fractal theory,and then establishes a prediction model based on fractal interpolation.After analyzing the prediction error of the basic model,first of all,by making appropriate improvements to the method of inform interpolation to improve the prediction efficiency of the inform interpolation model,and comparing with the prediction error of the daily air quality index of ARIMA,SVR,BP neural network and other models in Shanghai,the empirical results show that it is very effective to apply the insalient interpolation to AQI prediction.In addition,this article combines the method of insalication with the neural network algorithm to construct a hybrid prediction model based on the interpolation of the inform.By analyzing the prediction results obtained by applying the model in two prediction scenarios,it shows that the hybrid prediction model has excellent prediction performance in air quality prediction.