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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
