Support Vector Regression Model of Chlorophyll-&amp;lt;i&amp;gt;a&amp;lt;/i&amp;gt; during Spring Algal Bloom in Xiangxi Bay of Three Gorges Reservoir, China

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

doi:10.4236/jep.2012.35052

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.

Highlights

The eutrophication of reservoirs and lakes has been a major water quality problem for decades, causing turbid water with high algal biomass [1,2,3]
In water research and management, chlorophyll-a is the fundamental index of phytoplankton abundance and a good indicator of algal bloom [5,6,7]
The algal bloom is the multivariate interaction and nonlinear process. Linear based methods such as multiple linear regression (MLR) are not able to represent satisfactorily the correlation between chlorophyll-a and respective environmental variables, because these methods do not account for non-linearity

Summary

Introduction

The eutrophication of reservoirs and lakes has been a major water quality problem for decades, causing turbid water with high algal biomass [1,2,3]. This trend is expected to increase by high levels of nutrient input as a result of human activities such as sewage and storm overflows, runoff of commercial fertilizer, and so on [4]. The algal bloom is the multivariate interaction and nonlinear process. Linear based methods such as MLR are not able to represent satisfactorily the correlation between chlorophyll-a and respective environmental variables, because these methods do not account for non-linearity. ANN can in principle model nonlinear relations but often difficult to train or even yield unstable models and another drawback is the fact that ANN does not lead to one global or unique solution due to differences in their initial weight set

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Results