Prediction of Millers Ferry Dam Reservoir Level in USA Using Artificial Neural Network

Authors

  • Fatih Üneş
    Affiliation

    Mustafa Kemal University, Civil Engineering Faculty

  • Mustafa Demirci
    Affiliation

    Mustafa Kemal University, Civil Engineering Faculty

  • Özgür Kişi
    Affiliation

    Canik Basari University, Faculty of Architecture and Engineering, Civil Engineering Department,

https://doi.org/10.3311/PPci.7379

Abstract

Reservoir level modeling is important for the operation of dam reservoir, design of  hydraulic structures, determining pollution in reservoir and the safety of dam. In this study, daily reservoir levels for Millers Ferry Dam on the Alabama River in USA were predicted using artificial neural networks (ANN). Bayesian regularization backpropagation training algorithm is employed for optimization of the network. The results of the optimal ANN models were compared with conventional auto-regressive models (AR), auto-regressive moving average (ARMA), multi-linear regression (MLR) models. The models are compared with each other according to the three criteria, namely, mean square errors, mean absolute relative error and correlation coecient. The comparison results show that the ANN models perform better than the  conventional models.

 

Keywords:

Reservoir level, prediction, artificial neural network, auto-regressive moving average

Published Online

2015-04-17

How to Cite

Üneş, F., Demirci, M., Kişi, Özgür “Prediction of Millers Ferry Dam Reservoir Level in USA Using Artificial Neural Network”, Periodica Polytechnica Civil Engineering, 59(3), pp. 309–318, 2015. https://doi.org/10.3311/PPci.7379

Issue

Vol. 59 No. 3 (2015)

Section

Research Article