(untitled) (Malaysian Journal of Civil Engineering, 2018)
The objective of this study was to develop a multilayer perceptron neural network (MLPNN) and radial basic function neural network (RBFNN) model to predict the dissolved oxygen (DO) at some selected locations at the Kainji hydropower reservoir, Nigeria. The neural networks (NN) model was developed using water quality data collected over a six-year period (2010 to 2015). The NN structure was designed and trained using the SPSS neural network toolbox. The input variables to the NN were: pH, temperature, chloride (Cl-), PO43-, NO3-, Fe2+, and electrical conductivity (EC), while the output was the DO. The performance evaluation of the model was carried out using the coefficient of correlation (r), mean square error (MSE) and mean relative error (MRE). A positive correlation was observed between the actual and simulated DO at the four locations. The results of the simulation showed that the application of the NN and multiple regression analysis to predict DO concentration in water gave satisfactory results for all the selected locations using the two NN modeling approaches. Thus it has been demonstrated that NN modeling tools and multiple regression analysis are very efficient and useful for the computation of water quality parameters.