Biochemical oxygen demand (BOD) is an important parameter for measuring the water quality especially the extent of water pollution due to organic compounds. The standard test for BOD requires a time period of 5 days with stringent conditions to be observed with regards to temperature, nutrients available and the lighting conditions suitable for the microbial growth. In order to predict BOD of river water in a cost-effective and efficient manner, in this paper a data driven ensemble method namely a Rotation Forest (RF) has been implemented. The model uses model trees M5 as base learners and hence the name rotation forest. Each base learner is trained using the rotated feature axes built on feature subsets computed using Principal Component Analysis (PCA). This helps to improve diversity in training the base learners and hence improves the predictive accuracy. Experimental analysis on available data sets shows that the correlation coefficient of a proposed approach is 0.9386 and RMSE of 0.5388. The predictive accuracy of this model is also compared with Multilayer Perceptron (MLP) neural networks model. However the proposed model has high correlation coefficient and low RMSE than MLP.

BOD, rotation forest, ensemble,M5,MLP, PCA,Correlation Coeffecient,RMSE

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