Beijing Air Quality Forecast And Long-term Trend Analysis Based On Machine Learning

With the continuous development of society and economy,people’s living standards have greatly improved,but the growing environmental problems have had many negative effects on people’s quality of life.As one of the fastest growing cities in the developing world,Beijing has been committed to finding ways to improve air quality.Therefore,it is of great research significance to explore the air quality laws of Beijing’s urban areas and implement accurate predictions.Firstly,based on real-time data of air pollutants at 35 environmental monitoring points in Beijing,data mining was performed to explore the air quality and pollutant change laws of Beijing’s urban areas from the perspective of time and space.Secondly,machine learning methods were used for neural networks.Methods Existing problems such as over-fitting or being easily trapped in local optimization in the prediction.The random forest regression method and the limit random tree method were introduced.Based on the correlation analysis between Beijing urban air quality and influencing factors,a prediction model of air quality index for each urban Beijing was constructed.The results show that using random forest regression and extreme random tree methods can avoid the occurrence of overfitting,and when the accuracy of the prediction is substantially the same,compared with the complex network structure of neural networks,random forest regression and extreme random tree methods It is simpler,and the prediction results using the extreme random tree method have higher accuracy.Finally,for the long-term development trend of air quality in Beijing urban areas,the differences between the World Health Organization(WHO)"Air Quality Guidelines" and China’s air quality standards are summarized,calculate the difference between each pollutant index and the secondary and primary standards of air quality,find out the main factors affecting air quality according to the proportion of air quality.To meet the standards gap,and put forward suggestions from the actual point of view for air pollution control.