Using Support Vector Regression Algorithm to Predict Chlorophyll-a Concentrations with Chenghai Lake for Example

The support vector regression (SVR) algorithm was used to predict the concentration of chlorophyll-a (Chl-a) of eutrophication water in Chenghai Lake, and the leave-one-out cross-validation (LOOCV) method was used to optimize the model parameters. Then the prediction accuracy of SVR model was discussed on the basis of the mean relative error (MRE). The results demonstrated that the SVR model built by radial basis kernel function (RBF) had the optimal predictive ability. The predicted values of SVR were in good consistency with the measured values of experiment. The correlation coefficient (R) and MRE of SVR model could reach 0.938 and 12.30%, respectively. It was found that the modeling results of SVR were better than that of back propagation arti?cial neural networks (BP-ANN), suggesting that SVR was a valuable tool for the prediction of Chl-a.