Title :
A combination of Box-Jenkins analysis and neural networks to model and predict water consumption in Kuwait
Author :
Smaoui, Nejib ; BuHamra, Sana ; Gabr, Mahmoud
Author_Institution :
Dept. of Math. & Comput. Sci., Kuwait Univ., Safat, Kuwait
fDate :
6/24/1905 12:00:00 AM
Abstract :
Two approaches, namely Box-Jenkins approach and artificial neural networks approach (ANN) are combined to model time series data of water consumption in Kuwait. The Box-Jenkins 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 backpropagation 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 Box-Jenkins 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
Keywords :
autoregressive moving average processes; backpropagation; feedforward neural nets; forecasting theory; time series; water supply; Box-Jenkins analysis; Kuwait; autoregressive integrated moving average model; backpropagation; feedforward neural networks; forecasting; time series modelling; water consumption; Artificial neural networks; Chaos; Delay effects; Fractals; Intelligent networks; Mathematical model; Mathematics; Neural networks; Predictive models; Time series analysis;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007770