Abstract
Two approaches, namely the Box–Jenkins (BJ) approach and the artificial neural networks (ANN) approach were combined to model time series data of water consumption in Kuwait. The BJ approach was used to predict unrecorded water consumption data from May 1990 to December 1991 due to the Iraqi invasion of Kuwait in August 1990. A supervised feedforward back-propagation neural network was then designed, trained and tested to model and predict water consumption from January 1980 to December 1999. It is interesting to note that the lagged or delayed variables obtained from the BJ approach and used in neural networks provide a better ANN model than the one obtained either blindly in blackbox mode as has been suggested or from traditional known methods.
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