Research And Application Of Interval Prediction Model About Air Quality Index

Air pollution is becoming more and more dangerous to people's life,health,production and living style recently.There is no delay in enhancing the ability of monitoring and improving the level of governing the air pollution.So,a hybrid neural network model about interval forecasts of Air Quality Index is proposed in this article.It is more advantageous than traditional models of certain predictive values in dealing with this nonlinear problem,because the latter cannot properly handle so many uncertain factors.The proposed model involves four main steps.Firstly,the method of interpolating mean value is used to interpolate the missing values in the original sample sequence.Secondly,the method of wavelet decomposition is used to decompose the sequence which is completed.Thirdly,the normalized sequences of each group are respectively subjected to the interval prediction by Back Propagation Neural Network(BPNN)based on the Gray Wolf Optimization(GWO).The predicted upper and lower bounds of each group of sequences are output,then add them together to get the final forecast interval respectively after the results of the anti-normalization are processed.Finally,comparing the results of this proposed model with other reference models synthetically according to the three major corresponding indexes.The innovation of this paper is to propose a new decomposition and integration model,and make a different interval confidence prediction for studying the sequence.This will not only allow sufficient comparison between different models,but will also help to identify models that perform better on this prediction problem,and will also help people to more fully grasp the model's prediction results.The results of empirical study show that the proposed model is more valid,accurate,simple and useful than other models in the interval prediction of Air Quality Index.