基于机器学习的PM2.5短期浓度动态预报模型

摘要

The forecasted concentration of PM2.5 forecasting model greatly deviate from the measured concentration.In order to solve this problem,the data (from February 2015 to July 2015),consisting of measured PM2.5 concentration,PM2.5 model (WRF-Chem) forecasted concentration and model forecasted data of 5 main meteorological factors,were provided by Shanghai Pudong Meteorological Bureau.Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) algorithm were combined to build rolling forecasting model of hourly PM2.5 concentration in 24 hours in advance.Meanwhile,the nighttime average concentration,daytime average concentration and daily average concentration during the upcoming day were forecasted by roiling model.Compared with Radical Basis Function Neural Network (RBFNN),Multiple Linear Regression (MLR) and WRF-Chem,the experimental results show that the proposed SVM model improves the forecasting accuracy of PM2.5 concentration one hour in advance (according with the results concluded from finished research),and can comparatively well forecast PM2.5 concentration in 24 hours in advance,and effectively forecast the nighttime average concentration,daytime average concentration and daily average concentration during the upcoming day.In addition,the proposed model has comparatively high forecasting accuracies of hourly PM2.5 concentration in 12 hours in advance and nighttime average concentration during the upcoming day.%针对目前现有的PM2.5模式预报系统的预报值偏离实际浓度较大的问题,从上海市浦东气象局获得2015年2月至7月的PM2.5实况观测浓度、PM2.5模式预报(WRF-Chem)浓度和5个主要气象因子的模式预报数据资料,联合应用支持向量机(SVM)和粒子群优化(PSO)算法建立滚动预报模型,对PM2.5未来24小时浓度进行预报,同时对未来一天的昼、夜均值及日均值浓度进行预报,并与径向基函数神经网络(RBFNN)、多元线性回归法(MLR)、模式预报(WRF-Chem)作对比.实验结果表明,相比其他预报方法,所提出的SVM模型较大提高了PM2.5未来1小时浓度预报精度,这与此前的研究结论相符;所提模型能对PM2.5未来24小时浓度进行较好的预报,能对未来一天的昼均值、夜均值及日均值进行有效预报,并且对未来12小时的逐时浓度及未来一天的夜均值浓度的预报准确度较高.