Title: An Application Support Vector Machine Model (SVM) Technique for Biochemical Oxygen Demand (BOD) Prediction

Year of Publication: Nov - 2014
Page Numbers: 209-212
Authors: Ali Najah Ahmed , and A. El-Shafie
Conference Name: The International Conference on Artificial Intelligence and Pattern Recognition (AIPR2014)
- Malaysia

Abstract:


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.