Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs)

The objective of this study is to develop a feed forward neural network (FFNN) model and a radial basis function neural network (RBFNN) model to predict the dissolved oxygen from biochemical oxygen demand (BOD) and chemical oxygen demand (COD) in the Surma River, Bangladesh. The neural network model was developed using experimental data which were collected during a three year long study. The input combinations were prepared based on the correlation coefficient with dissolved oxygen. Performance of the ANN models was evaluated using correlation coefficient (R), mean squared error (MSE) and coefficient of efficiency (E). It was found that the ANN model could be employed successfully in estimating the dissolved oxygen of the Surma River. Comparative indices of the optimized RBFNN with input values of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) for prediction of DO for testing array were MSE = 0.465, E = 0.905 and R = 0.904 and for validation array were MSE = 1.009, E = 0.966 and R = 0.963. Comparing the modeled values by RBFNN and FFNN with the experimental data indicates that neural network model provides reasonable results.