基于特征向量的最小二乘支持向量机PM2.5浓度预测模型  

PM2. 5 concentration prediction model of least squares support vector machine based on feature vector

作　　者：李龙[1] 马磊[1] 贺建峰[1] 邵党国[1] 易三莉[1] 相艳[1] 刘立芳[1] 

机构地区：[1]昆明理工大学信息工程与自动化学院,昆明650500

出　　处：《计算机应用》2014年第8期2212-2216,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(11265007);教育部留学回国人员科研启动基金资助项目(2010-1561)

摘　　要：针对大气中细颗粒物(PM2.5)浓度预测的问题,提出一种预测模型。首先,通过引入综合气象指数综合考虑风力、湿度、温度等因素;然后,结合实际二氧化硫(SO2)浓度、二氧化氮(NO2)浓度、一氧化碳(CO)浓度和PM10浓度等,构成特征向量;最后,利用特征向量和PM2.5浓度数据来建立最小二乘支持向量机(LS-SVM)预测模型。经2013年城市A和城市B环境监测中心的数据预测分析表明,引入综合气象指数后预测的准确性提高,误差降低近30%。说明该模型能够较为准确地预测PM2.5浓度,并具有较高的泛化能力。此外还分析了PM2.5浓度与住院率、医院门诊量的关系,发现了它们的高度相关性。To solve the problem of Fine Particulate Matter （PM2.5） concentration prediction, a PM2.5 concentration prediction model was proposed. First, through introducing the comprehensive meteorological index, the factors of wind, humidity, temperature were comprehensively considered; then the feature vector was conducted by combining the actual concentration of SO2, NO2, CO and PM10; finally the Least Squares Support Vector Machine （LS-SVM） prediction model was built based on feature vector and PM2.5 concentration data. The experimental results using the data from the city A and city B environmental monitoring centers in 2013 show that, the forecast accuracy is improved after the introduction of a comprehensive weather index, error is reduced by nearly 30%. The proposed model can more accurately predict the PM2.5 concentration and it has a high generalization ability. Furthermore, the author analyzed the relationship between PM2.5 concentration and the rate of hospitalization, hospital outpatient service amount, and found a high correlation between them.

关 键 词：PM2 5浓度预测 综合气象指数 特征向量 相关性分析 最小二乘支持向量机 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]