Air Quality Prediction Based on Random Forest and Neural Networks
Due to different influencing factors,the air quality in different regions usually exhibits different characteristics of change.Because the duration of pollutants has a certain impact on the prediction,the historical data of six major pollutants(PM2.5,PM10,SO2,NO2,CO,O3)in air quality monitoring in Lanzhou City are divided into seven sets of feature sets at different times,and the fitting effect of several single models on air quality prediction on seven sets of feature sets is compared and analyzed.It is found that the single model with the best fitting effect is the neural network model.Considering the impact of feature selection on air quality prediction,this paper establishes a random forest combination neural network model(RF+MLP)to predict the air quality index.Finally,the fitting results of the three models(RF+MLP,RRFMLP,MLP)for air quality index prediction were compared and analyzed.The results showed that the RF+MLP model had the best overall fitting degree for predicting the air quality index in Lanzhou City.
air quality indexrandom forests modelthe neural network modelcombination model
万方数据
维普
