基于特征向量的最小二乘支持向量机PM2.5浓度预测模型

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。

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

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.