Support Vector Regression Model of Chlorophyll-<i>a</i> during Spring Algal Bloom in Xiangxi Bay of Three Gorges Reservoir, China — Oak Academic Publishing
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Support Vector Regression Model of Chlorophyll-<i>a</i> during Spring Algal Bloom in Xiangxi Bay of Three Gorges Reservoir, China
College of Chemistry & Life Science, Three Gorges University, Yichang, China
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College of Hydroelectric & Environment, Three Gorges University, Yichang, China
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Engineering Research Center of Eco-Environment in Three Gorges Reservoir Region, Ministry of Education, Three Gorges University, Yichang, China
1 College of Chemistry & Life Science, Three Gorges University, Yichang, China
2 College of Hydroelectric & Environment, Three Gorges University, Yichang, China
3 Engineering Research Center of Eco-Environment in Three Gorges Reservoir Region, Ministry of Education, Three Gorges University, Yichang, China
To study the relationship between chlorophyll-a and environmental variables during spring algal bloom in Xiangxi Bay of Three Gorges Reservoir, the support vector regression (SVR) model was established. In surveys, 11 stations have been investigated and 264 samples were collected weekly from March 4 to May 13 in 2007 and February 16 to May 10 in 2008. The parameters in SVR model were optimized by leave one out cross validation. The squared correlation coefficient R 2 and the cross validated squared correlation coefficient Q 2 of the optimal SVR model are 0.8202 and 0.7301, respectively. Compared with stepwise multiple linear regression and back propagation artificial neural network models using external validation, the SVR model has been shown to perform well for regression with the predictive squared correlation coefficient R 2 pred value of 0.7842 for the test set.
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