Dam reservoir level modeling by neural network approach: A case study
Neural Network World, vol.20, no.4, pp.461-474, 2010 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 20 Issue: 4
- Publication Date: 2010
- Journal Name: Neural Network World
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.461-474
- Keywords: Artificial Neural Networks (ANNs), Dam, Model, Multi layer perceptron, Prediction, Reservoir level
- Hatay Mustafa Kemal University Affiliated: Yes
Abstract
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.