The optimal SVR model was compared with stepwise MLR and back propagation artificial neural network (BPANN). The parameters of BPANN model with three layers used were as follows: the number of hidden nodes was seven; the transformation function was sigmoid; the learning rate and momentum of each epoch were set to 0.30 and 0.20 respectively. External validation was used to compare the predictive capacity of models. The data set was randomly classified into training set (80% data) and test set (20% data) and the predictive () values were calculated according to the following equation:

(8)

Figure 2. versus and C with RBF kernel function (σ = 0.10).

Figure 3. versus and C with polynomial kernel function.

Figure 4. versus and C with linear kernel function.

Figure 5. Observed values versus calculated values for chlorophyll-a using optimal SVR model.

where and represent the predicted and observed chlorophyll-a values of the test set, respectively. is the mean chlorophyll-a value of the training set.

The values of MLR, BPANN and SVR models were 0.4127, 0.7644 and 0.7842 respectively. Figure 6 shows the predicted values of optimal SVR model versus observed values for chlorophyll-a. Based on the above results. The SVR method has been shown to perform well for regression and be a useful and powerful technique to construct the chlorophyll-a model during spring algal bloom.

4. Conclusion

The support vector regression model of chlorophyll-a during spring algal bloom in Xiangxi Bay of Three

Figure 6. Observed values versus predicted values for chlorophyll-a using optimal SVR model.

Gorges Reservoir was established. Using stepwise MLR method, the important environmental variables (DO, pH, Turb, PO_{4}, NH_{4}, NO_{3} and SiO_{4}) were selected. The parameters in SVR such as the type of kernel function, the regularization parameter and -insensitive loss function were optimized by leave one out cross validation. and of the optimal SVR model are 0.8202 and 0.7301, respectively. Compared with MLR and BPANN models, the SVR model has been shown to perform well for regression with the value of 0.7842 for the test set.

5. Acknowledgements

This work was funded by National Natural Science Foundation of China (No. 50679038, 51009080), National Science and Technology Support Program of China (No. 2008BAB29B09), National Water Special Project of China (2008ZX07104-004) and Hubei Province Ministry of Environmental Protection, China (No. 2008HB08). We thank Daobin Ji, Zhengjian Yang, Zhongqiang Yi, Jun Ma, Yanmei Su, Xia Yang and Qiaoli Cao for their assistance in the field and lab.

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