An application Support Vector Machine model technique for Biochemical Oxygen Demand Prediction

A Najah, Dept. Engineering Science, University Malaysia Terengganu and A. El-Shafie, Dept. Civil & Structural Eng, University Kebangsaan Malaysia


In this study, Support Vector Machine (SVM) technique has been investigated in prediction of Biochemical Oxygen Demand (BOD). To assess the effect of input parameters on the model, the sensitivity analysis was adopted. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Correlation Coefficient (CC), Mean Square Error (MSE) and Correlation of Efficiency (CE). The principle aim of this study is to develop a computationally efficient and robust approach for predict of BOD which could reduce the cost and labour for measuring these parameters. This research concentrates on the Johor River in Johor State, Malaysia where the dynamics of river water quality are significantly altered. The result showed that the proposed model (SVM) improved the precision of BOD prediction with minimal computation. The tools used in this work could form a basis for a more effective decision making process on the part of the policy makers in order to help maintain and improve the management of river basins.

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