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K.-S. Jeong, D.-K. Kim and G.-J. Joo, “River Phytoplankton Prediction Model by Artificial Neural Network: Model Performance and Selection of Input Variables to Predict Time-Series Phytoplankton Proliferations in a Regulated River System,” Ecological Informatics, Vol. 1, No. 3, 2006, pp. 235-245. doi:10.1016/j.ecoinf.2006.04.001

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• TITLE: Support Vector Regression Model of Chlorophyll-a during Spring Algal Bloom in Xiangxi Bay of Three Gorges Reservoir, China

  AUTHORS: Hua-Jun Luo, De-Fu Liu, Ying-Ping Huang

  KEYWORDS: Support Vector Regression; Chlorophyll-A; Algal Bloom; Three Gorges Reservoir

  JOURNAL NAME: Journal of Environmental Protection, Vol.3 No.5, May 30, 2012

  ABSTRACT: 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 R2 and the cross validated squared correlation coefficient Q2 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 R2pred value of 0.7842 for the test set.