Prediction of WQI for Tha Chin River Using an Ensemble of Support Vector Regression and Complementary Neural Networks
نویسندگان
چکیده
This paper proposes an ensemble of support vector regression and complementary neural networks to predict the Water Quality Index (WQI) for Tha Chin River, Nakhonpathom, Thailand. The data set was collected during the year 2002-2007. Four parameters are used in the prediction which are dissolved oxygen, total solid, biological oxygen demand, and suspended solid. It is found that an ensemble of support vector regression and complementary neural networks provides better accuracy result than results obtained from individual techniques: support vector regression, complementary neural networks, mathematical model based on genetic algorithm, and the equation used by the Pollution Control Department of Thailand. Key–Words: Backpropagation Neural Network, Complementary Neural Networks, Support Vector Regression, Genetic Algorithm, Ensemble, Water Quality Index.
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