Comparative analysis of neural network techniques for predicting water consumption time series

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.