Water use prediction by radial and feed-forward neural nets
Proceedings of the Institution of Civil Engineers: Water Management, cilt.162, sa.3, ss.179-188, 2009 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 162 Sayı: 3
- Basım Tarihi: 2009
- Doi Numarası: 10.1680/wama.2009.162.3.179
- Dergi Adı: Proceedings of the Institution of Civil Engineers: Water Management
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.179-188
- Anahtar Kelimeler: hydrology & water resource, municipal & public service engineering, water supply
- Manisa Celal Bayar Üniversitesi Adresli: Evet
Özet
In this study, applicability of feed-forward and radial-basis neural networks for monthly water consumption prediction from several socio-economic and climatic factors affecting water use is investigated. A data set including a total of 108 data records is divided into two subsets: training and testing. Firstly, the models based on a single input variable are trained and tested by feed-forward and radial methods and feed-forward and radial performances of the models are compared. Then, the models based on multiple input variables are constructed according to performances of the models based on a single input variable. The performances of feed-forward and radial models in training and testing phases are compared with the observations and the best-fit model is identified. For this purpose, several criteria such as normalised root mean square error, efficiency and correlation coefficient are calculated for all models. Subsequently, the best-fit models are also trained and tested by multiple linear regression for comparison. The results indicated that feed-forward and radial methods can be applied successfully for monthly water consumption prediction. © 2009 Thomas Telford.