Dam reservoir level modeling by neural network approach: A case study
[ N/A ]
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Prediction of reservoir level fluctuation is important in the operation, design, and security of dams. In this paper, Artificial Neural Networks (ANN) is used for modeling. In such modeling approaches, it is possible to determine dam reservoir level and water balance (budget) by taking the monthly average precipitation and needed parameters into consideration. The basic data are available for over 29 years at the Tahtaköprü Dam in the southeast Mediterranean region of Turkey. As a sub-approach of ANN, a multi layer perceptron (MLP) is used. Bayesian regularization back-propagation training algorithm is employed for optimization of the network. MLP results are compared with the results of conventional multiple linear regression (MLR) and autoregressive (AR) models. The comparison shows that the ANN model provides better performance than the mentioned models in reservoir level estimation. ©ICS AS CR 2010.
Açıklama
Anahtar Kelimeler
Artificial Neural Networks (ANNs), Dam, Model, Multi layer perceptron, Prediction, Reservoir level
Kaynak
Neural Network World
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
20
Sayı
4
