Abstract
In this study Feed Forward Back propagation (FFBP) and Cascade Feed Forward Back propagation (CFBP) algorithms have been compared for prediction of water quality in the distribution systems, so as make it tomeet the standards of sustainability. These algorithms have been used for forecasting Water Quality Index (WQI) in various zones of municipal distribution system by using pH, alkalinity, hardness, dissolved oxygen (DO), total solids (TS) and MPN as the input variables. Different ANN models were developed using training data set and tested in order to determine optimum number of neurons in the hidden layer, best fitting transfer function, optimum length of training dataset and best suited ANN algorithm. Further the developed ANN models were compared with multiple linear regression (MLR) technique, which is a commonly used statistical technique for modelling water quality variables. The study revealed that ANN model outperforms multiple regression technique for prediction of water quality in the distribution system and it is a robust tool for understanding the poorly defined relations between water quality variables and WQI in municipal distribution system.
Published Version
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