Comparative analysis of neural network techniques for predicting water consumption time series
Küçük Resim Yok
Tarih
2010
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Science Bv
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. (C) 2010 Elsevier B.V. All rights reserved.
Açıklama
Anahtar Kelimeler
Water consumption, Time series, ANN, GRNN, FFNN, CCNN
Kaynak
Journal of Hydrology
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
384
Sayı
1-2
