PREDICTION OF TURBIDITY IN TIGRIS RIVER USING ARTIFICIAL NEURAL NETWORKS

Abstract

Over the past two decades, there has been an increased interest in a new class of computational intelligence systems known as Artificial Neural Networks (ANNs). In this work, (ANNs) technique was applied in an attempt to predict the turbidity at intake of Al-Wathba water treatment plant (WTP) in Baghdad. This prediction is useful in the planning, evaluation, management, and operation of such plants, which may produce water of better quality. The available records from (1991-2000) were used for predicting turbidity in Tigris River, based on monthly maximum values of the water quality parameters near intakes of the water treatment plants. Multi-layer perceptron trainings using the back-propagation algorithm were used in this work. The feasibility of ANNs technique for modeling this water quality parameter was investigated. A number of issues in relation to ANNs construction such as the effect of ANNs geometry and internal parameters on the performance of ANNs model were investigated. It was found that ANNs have the ability to predict the Turbidity at Al-Wathba WTP with a good degree of accuracy (the coefficient of determination (R2) was 0.9687). The ANNs model developed to study the impact of the internal network parameters on model performance indicate that ANNs performance was relatively insensitive to the number of hidden layer nodes, momentum term, and learning rate.