基于多元线性回归的雾霾预测方法研究

提出了一种在线样本更新的多元线性回归分析的雾霾预测方法。首先搜集了北京市天气状况,包括平均气温、湿度、风级等气象数据以及PM2.5、CO、NO₂、SO₂等大气成分浓度数据,然后通过散点图对这些因素进行主要影响因素分析,筛选出对雾霾影响比较明显的因素作为雾霾预测的依据。通过在线样本更新的多元线性回归建立了PM2.5含量预测模型,并将气象要素作为雾霾的判断标准。最后给出实际例子,利用多元线性回归对北京未来一天、三天及一周的PM2.5含量进行较为精确的预测。

Research on Haze Prediction Based on Multivariate Linear Regression

This paper presented a method to predict the haze based on multiple linear regression analysis,whose sample is online update.First,the data of Beijing weather conditions are collected,including average temperature,humidity,wind and other meteorological data level and PM2.5,CO,NO₂,SO₂ and other atmospheric concentration data.Then,the main influencing factors are analyzed by scatter plot of these factors,and the main factors,which have a great effect on the haze,are selected as the haze forecast basis.Furthermore,PM2.5 prediction model is built by multiple linear regression method.The prediction results combined with meteorological factors are in the form of the criterion of haze judgment.Finally,this paper provided an actual example for haze prediction,which utilizes multiple linear regression to forecast the weather condition of Beijing after one day,three days and seven days.