Prediction of millers ferry dam reservoir level in USA using artificial neural network

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 coefficient. The comparison results show that the ANN models perform better than the conventional models.