Prediction of water quality index (WQI) based on artificial neural network (ANN)

This paper investigates the effectiveness of artificial neural network models for predicting the water quality index for rivers in Malaysia. The network was trained with reference to seven major parameters for the determination of the water pollutant index, water quality index and water quality class, for Malaysian rivers in Pahang and Selangor. The data collected comprises of data for the previous three years, beginning from 1999. The water quality index plays an important role in evaluating the water quality of rivers. The artificial neural network simplifies and speeds up the computation of the water quality index, as compared to the currently existing method. By optimizing the calculation, a significant saving in terms of money and time can be achieved. Artificial neural network models with different learning approaches, such as back propagation neural network, modular neural network and radial basis function network, are considered and adopted to model the water quality index.