Air Quality Analysis And Prediction Based On Machine Learning

In recent years,with the increase of haze weather,the public's attention has gradually focused on air quality.At present,air quality has become one of the environmental issues of common concern to society and the public.Various factors such as the distribution of pollution sources,meteorological factors,and types of pollutants affect the level of pollutant concentrations in urban air,and the distribution characteristics of pollutant concentrations in different cities are different.Therefore,indepth discussion of the relationship between pollutant concentrations and the relationship between meteorological factors and pollutant concentrations,the prediction of PM2.5 concentration,the study of urban environmental pollution,air quality problems,and the study of effective preventive measures for such problems It is of great significance.Based on the topic "Research and Application of Pollutant Distribution Modeling Technology Based on Big Data of Air Quality Monitoring" and taking air pollutant concentration as the research object,this paper proposes a PM2.5 concentration prediction model based on ARIMA-SVM.The accuracy of PM2.5 concentration prediction was improved;and an air quality prediction system was implemented on this basis.The main research contents include:(1)Preprocessing of air pollutant data: To solve the problem of missing values in the data,multi-interpolation is used to fill the missing values;use the K-means clustering algorithm in machine learning to identify outliers,generate labels from six types of pollutant data,and then cluster the data according to the cluster center to delete outliers.Generate labels,and then cluster the data into six categories based on the cluster center,and delete the abnormal points.In this way,high-quality and representative data are compiled.(2)Correlation analysis between attrebutes: The Spearman rank correlation coefficient was used to analyze the correlation between pollutants and the correlation between pollutants and meteorological factors;a linear regression model was established between PM2.5 concentration and other five pollutant concentrations,which verified Conclusion of Spearman rank correlation coefficient.(3)Construction of PM2.5 concentration prediction model: Focused on the ARIMA and SVM algorithms,and combining these two algorithms to build a PM2.5 concentration prediction model based on ARIMA-SVM.This model combines SVM with ARIMA,which makes up for the lack of traditional ARIMA time series predictions that cannot handle non-linear data and improves the accuracy of the prediction.At the same time,a Keras-based LSTM neural network model is used to predict PM2.5 concentration;The ARIMA-SVM combination model is compared with the LSTM model.(4)Air pollutant monitoring system: Designed and implemented an air pollutant monitoring system using tools such as Pycharm and Hbuilder.The system implements functions such as city search,city AQI index ranking,display of pollutant concentration at each detection point in the city,and AQI change trend.