# Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP
## 4 Conclusions  
In this study, we constructed an air quality level prediction model based on STEPDISC and PCA optimized BP neural network for the daily average monitoring values of air pollutants and meteorological related factors and air quality level label samples in Bayannur city from 2015 to 2020. We used 1240 sets of samples to train the model, and a dataset with a large sample size can improve the robustness of the model. Firstly, at the significance level of 0.01, nine factors with significant ability to distinguish air quality level were screened from twelve original factors using STEPDISC method, including air pressure, wind speed, sunshine hours, relative humidity, PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), and CO. PM\({}_{2.5}\) was moderately positively correlated with PM\({}_{10}\), NO\({}_{2}\) and SO\({}_{2}\) were moderately positively correlated with CO, and sunshine hours were moderately negatively correlated with relative humidity. Secondly, under the contribution threshold of 0.85, the principal component analysis was performed on the nine discriminative significant factors, and the top five principal components were indicated as sulfur and nitrogen carbon oxide pollution, particulate pollution, sunshine hours, air pressure, and wind speed, with contribution rates of 37.6%, 19.2%, 15.3%, 8.8%, and 7.7%, respectively. The extraction of the top five principal components can explain 88.6% of the information of the nine factors, effectively reduce the factor dimensionality, remove the correlation, and improve the overall interpretability of the result. Sulfur and nitrogen carbon oxide pollution emissions are higher from December to February and lower from May to July. Particulate pollution is most serious in April and May, and gradually reduced from December to February; both types of pollution are light in September, and the air quality is mainly excellent and good. Finally, the scores of each group of samples in the five principal components were calculated, and the normalized principal component score vectors were input into the BP neural network for air quality level prediction modeling. With the same network training parameter configuration, the prediction accuracy of the STEPDISC-PCA-BP, PCA-BP, and BP models was 85.5%, 61.77%, and 56.69%, respectively. The STEPDISC-PCA-BP model has a simpler structure, higher  
Figure 9: Comparison results of performance evaluation of STEPDISC-PCA-BP, PCA-BP, and BP models. prediction accuracy, shorter time consumption, better network performance, stronger generalization ability, the faster convergence speed and the performance is better when applied to the study of local air quality prediction. Research on air quality prediction algorithms such as decision tree, convolutional neural network, and random forest can promote the application of machine learning in the environmental field, improve science and technology, and accelerate the process of environmental protection. Conceptualization, M.L. and H.H. ; data curation, M.L., Y.Z. and J.L. ; formal analysis, M.L., Y.Z. and J.L. ; funding acquisition, H.H. and L.Z. ; investigation, M.L. ; methodology, H.H. ; project administration, H.H. and L.Z. ; resources, H.H. ; software, M.L., L.Z. and J.L. ; supervision, H.H. ; validation, M.L., H.H. and Y.Z. ; visualization, M.L. ; writing--original draft, M.L. ; writing--review and editing, H.H. All authors have read and agreed to the published version of the manuscript.