DOIONLINE

DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-2692

Publish In
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN
Journal Home
Volume Issue
Issue
Volume-3, Issue-8  ( Aug, 2015 )
Paper Title
Generalized Regression Neural Networks For Reservoir Level Modeling
Author Name
Fatih Unes, Mustafa Demirci
Affilition
Iskenderun Technical University, Civil Engineering Department, Hydraulics Division, 31040, ─░skenderun, Hatay-Turkey
Pages
81-84
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 Generalized regression neural networks (GRNN). The results of the optimal GRNN models were compared with conventional multi-linear regression (MLR) model. 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 GRNN models perform better than the MLR model. Keywords: Reservoir level; Prediction; Generalized Regression Neural Networks, Multi-Linear Regression
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