Comparative analysis of neural network techniques for predicting water consumption time series
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Date
2010
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Abstract
Monthly water consumption time series have been predicted using a series of Artificial Neural Network (ANN) techniques including Generalized Regression Neural Networks (GRNN), Cascade Correlation Neural Network (CCNN) and Feed Forward Neural Networks (FFNN). One hundred and eight data sets for the city of Izmir, Turkey are used for a number of ANN modeling exercises. Several ANN models depending on the combination of antecedent values of water consumption records are constructed and the best fit input structure is investigated. The performance of ANN models in training and testing stages are compared with the observed water consumption values to identify the best fit forecasting model based upon a number of selected performance criteria. © 2010 Elsevier B.V. All rights reserved.
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Keywords
Izmir [Turkey] , Turkey , Time series , Time series analysis , Water analysis , Water supply , Artificial Neural Network , Best fit , Cascade correlation neural networks , CCNN , Comparative analysis , Data sets , Forecasting models , Generalized regression neural networks , Neural network techniques , Performance criterion , Training and testing , Water consumption , artificial neural network , comparative study , data set , time series , water budget , Neural networks