基于模糊时序和支持向量机的高速公路SO2浓度预测算法

针对现有SO₂浓度预测方法中存在的污染物来源和影响因素认识不统一、小样本数据敏感、易于陷入局部最优等问题，文中提出了基于模糊时序和支持向量机的高速公路SO₂浓度预测算法，为搭建高速公路环境健康监测系统提供了可靠的理论支持.该方法依据SO₂浓度的季节变动规律，以季节作为时间序列，以24h为粒化窗宽，通过高斯核函数提取原始样本数据的特征值，输入支持向量机训练模型，并利用k重交叉验证法结合网格划分优化模型参数.文中应用该方法建立了SO₂浓度预测模型，并以2014年4月至2015年3月山西省太旧高速公路某监测点SO₂小时浓度监测值为样本数据，在MATLAB平台下应用LIBSVM工具实现了计算过程.结果表明，基于模糊时序和支持向量机的高速公路SO₂浓度预测算法不受机理性理论研究的限制，支持小样本学习，非线性拟合效果好，泛化能力强.

Prediction for SO2 Concentration Based on the Fuzzy Time Series and Support Vector Machine (SVM) on Expressway

The present prediction methods for SO₂ concentration suffer from the disadvantages that there is no uniform understanding of pollutant sources and influencing factors, small sample data is sensitive, and prediction methods are easy to fall into local optimum etc. In order to solve these problems, a method for the prediction of SO₂ concentrations on expressway is proposed which is based on fuzzy time series and support vector machine (SVM), and provides a reliable theoretical support for building the highway environmental health monitoring system. Based on the seasonal variation of SO₂ concentrations, the method takes the season as time series, 24h for graining window width. Through the Gaussian kernel function to extract the eigenvalues of the original sample data, which are input support vector machine (SVM) model for training, and k-fold cross validation method combined with the grid division is used to optimize model parameters. Finally, a SO₂ concentrations prediction model is established with the method in this paper. By using 1h average SO₂ concentrations as sample data which are obtained by Shanxi taijiu expressway monitoring station from April 2014 to March 2014, the LIBSVM tool is used to realize the calculation process on the MATLAB platform. The results show that based on fuzzy time series and support vector machine (SVM), the forecasting methods of SO₂ concentration is not restricted by the research of machine rational theory, and supports small-sample learning, otherwise, the nonlinear fitting effect is perfect, and the ability of generalization is well.