Application of Artificial Neural Network For Defining The Water Quality in The River

Riyanto Haribowo, Very Dermawan, Nevandria Satrya Yudha


Predicting point and nonpoint source runoff of dissolved and suspended materials into their receiving streams is important to protecting water quality. Therefore, it is important to monitoring the condition of river water quality. The purpose of this study is to predict water quality in small streams using an Artificial Neural Network (ANN). The study focuses on small stream in tributary of Brantas River. The variables of interest are dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), pH and temperature (T). To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The result show that the prediction of DO is 6.03 mg/litre, pH is 6,47 mg/litre and temperature is 25.18°. With the relatively error was 15.63%, 12.64% and 14.12% respectively. It was finally concluded that ANN models are capable of simulating the water quality parameters.


artificial neural network; river; water quality; modelling

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