Research And Application Of Air Quality Index Short-term Forecast Model

With the breakthrough of big data,artificial intelligence and other technologies,their application fields are gradually involved in all aspects of people’s daily life.In recent years,the government departments and people pay more and more attention to environmental problems,which makes the air quality information closely related to it also attract people’s attention.Therefore,the accurate prediction of air quality information has become one of the current research hotspots.In this paper,on the premise of full analysis of the prediction method and related factors affecting air quality index,according to the related factors affecting air quality and data characteristics,the target is to predict the air quality index of a coal-power city.Three kinds of integrated algorithms are proposed based on neural network,support vector machine algorithm,time series analysis algorithm,thought evolution algorithm and simulated annealing algorithm.They are the integration algorithm of support vector machine and BP neural network,the integration algorithm of support vector machine and thought evolution,and the integration algorithm model of seasonal autoregressive average and support vector machine regression-The integrated algorithms of support vector machine and BP neural network include the integrated prediction model of support vector machine and BP neural network(SVM-BP)and the integrated prediction model of support vector machine and BP neural network(SVMR-BP),in which the BP neural network,support vector machine and support vector machine regression algorithm are optimized by simulated annealing algorithm.The problem of parameter optimization is solved.The integrated algorithm of support vector machine and mind evolution includes the integrated prediction model of support vector machine and mind evolution algorithm(SVM-MEA)and the integrated prediction model of support vector machine regression and mind evolution algorithm(SVMR-MEA).The autoregressive average and support vector machine regression integration algorithm is a combination of the autoregressive average model and support vector machine regression model with seasonality added after optimization,and integrates the seasonal autoregressive average and support vector machine regression integration algorithm prediction model(SARIMA-SVMR).In order to make the prediction results closer to the real results,after the prediction results of the above five integrated models are obtained,further fusion processing is carried out.A software prototype model is built on the basis of these algorithm models and the fusion algorithm to ensure that these integrated algorithm models can be applied to the local air quality index prediction.The experimental results show that the SVM-BP model and SVMR-BP model mentioned in this paper are more accurate in predicting results,and the SVM-MEA model is more accurate in predicting results when the air quality is good(50<AQI<100).The prediction results of SVMR-MEA model and SARIMA-SVMR model are closer to the real value than those of SVM-BP model and SVMR-BP model.After the fusion of the prediction results of all the algorithm models,the final result is almost the same as the actual value.Therefore,the air quality index prediction algorithm model and the prediction result processing method proposed in this paper can obtain the prediction result with high accuracy,which is suitable for the air quality index prediction of the cities selected in this paper.