Fuzzy Clustering Support Vector Machine for Predicting Regional PM2.5 Concentration
A fuzzy clustering support vector machine regression algorithm is proposed by analyzing and combining the characteristics of the fuzzy C-mean clustering algorithm and the support vector machine regression. The SVM is designed to forecast the particles density PM2.5 in the air. Firstly, a complex data set is separated and inserted into multiple groups using fuzzy C-mean clustering algorithm. Then the SVM regression model in each group is established. The integrated fuzzy clustering SVM regression is applied to forecast the PM2.5 in the local air. By comparing the predicted result with that of the self-organizing competitive neural network SVM regression model, as well as that of the single SVM regression model respectively, it is found that the predicted accuracy rate of the FCM-SVR is higher than that of the SOM-SVR model and SVR model.
fuzzy C-mean clusteringsupport vector machine regressionprediction of PM2.5 concentration
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