Dam reservoir level modeling by neural network approach: A case study

Title:

Dam reservoir level modeling by neural network approach: A case study

Creator:

Ünes, Fatih

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:f5f0c51a-e546-4c6b-b107-c770d19df3db
uuid:f5f0c51a-e546-4c6b-b107-c770d19df3db

Subject:

Artificial Neural Networks (ANNs), dam, reservoir level, prediction, multi layer perceptron, and model

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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 Tahtakőprű 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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:4

Harvested from:

CDK

Metadata only:

false