目前现有的PM2.5模式预报值偏离实况观测值较大。针对上述问题, 从上海浦东气象局获得2012年11月~2013年11月的PM2.5实况观测浓度、PM2.5模式预报(WRF-CHEM)浓度和主要气象影响因子的模式预报数据资料, 在PM2.5模式预报数据的基础上, 加入另外5个主要气象影响因子的模式预报数据, 应用支持向量机(SVM)建立动态预报模型, 提高PM2.5未来一小时浓度预报的精度, 并且与径向基神经网络(RBFNN)、多元线性回归法(MLR)、WRF-CHEM作对比。实验结果表明: 该算法较大提高了PM2.5未来一小时浓度预报的精度, 预报精度优于RBFNN、MLR和WRF-CHEM, 并且对PM2.5浓度变化剧烈的情况具有较好地预报能力。

Current PM2.5 model forecasting data greatly deviate from the measured concentration. In order to solve this problem, support vector machine (SVM) was applied to set up a dynamic model. The data of PM2.5 model forecasting (WRF-CHEM) concentration and the five main model forecasting meteorological factors were used as training data of SVM. The data were provided by Shanghai Meteorological Bureau in Pudong New Area (from November in 2012 to November in 2013). The dynamic model was used to improve the forecasting accuracy of PM2.5 concentration one hour in advance. SVM model was compared with radical basis function neural network (RBFNN), Multi-variable Linear Regression (MLR) and WRF-CHEM. Experimental results show that the proposed algorithm greatly improves the forecasting accuracy of PM2.5 concentration one hour in advance. SVM model performs better than RBFNN, MLR and WRF-CHEM, and has better forecasting ability for the condition with concentration dramatic changing.