Abstract:
River Nzoia in Kenya, due to its role in transporting industrial and municipal wastes in addition to agricultural
runoff to Lake Victoria, is vulnerable to pollution. Dissolved oxygen is one of the most important indicators of water
pollution. Artificial neural network (ANN) has gained popularity in water quality forecasting. This study aimed at
assessing the ability of ANN to predict dissolved oxygen using four input variables of temperature, turbidity, pH and
electrical conductivity. Multilayer perceptron network architecture was used in this study. The data consisted of 113
monthly values for the input variables and output variable from 2009–2013 which were split into training and testing
datasets. The results obtained during training and testing were satisfactory with R 2 varying from 0.79 to 0.94 and RMSE
values ranging from 0.34 to 0.64 mg/l which imply that ANN can be used as a monitoring tool in the prediction of
dissolved oxygen for River Nzoia considering the non-correlational relationship of the input and output variables. The
dissolved oxygen values follow seasonal trend with low values during dry periods.
