{"title":"基于多元线性回归的PM2.5浓度预测","authors":"J. Chen, Jianbo Wang","doi":"10.1109/ICSGEA.2019.00109","DOIUrl":null,"url":null,"abstract":"With the frequent occurrence of haze weather, the concentration prediction of PM2.5, the main pollutant in haze weather, has gradually become a hot topic. Based on the analysis of historical data of PM2.5 and related weather information in Changsha City, this paper establishes a multivariate linear regression model to predict the concentration of PM2.5. The validity of the model is verified by comparing the predicted value with the observed value. The model has a good application value for the prediction of PM2.5 concentration.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Prediction of PM2.5 Concentration Based on Multiple Linear Regression\",\"authors\":\"J. Chen, Jianbo Wang\",\"doi\":\"10.1109/ICSGEA.2019.00109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the frequent occurrence of haze weather, the concentration prediction of PM2.5, the main pollutant in haze weather, has gradually become a hot topic. Based on the analysis of historical data of PM2.5 and related weather information in Changsha City, this paper establishes a multivariate linear regression model to predict the concentration of PM2.5. The validity of the model is verified by comparing the predicted value with the observed value. The model has a good application value for the prediction of PM2.5 concentration.\",\"PeriodicalId\":201721,\"journal\":{\"name\":\"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSGEA.2019.00109\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSGEA.2019.00109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of PM2.5 Concentration Based on Multiple Linear Regression
With the frequent occurrence of haze weather, the concentration prediction of PM2.5, the main pollutant in haze weather, has gradually become a hot topic. Based on the analysis of historical data of PM2.5 and related weather information in Changsha City, this paper establishes a multivariate linear regression model to predict the concentration of PM2.5. The validity of the model is verified by comparing the predicted value with the observed value. The model has a good application value for the prediction of PM2.5 concentration.
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