International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Aug. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 87
Paper Published : 1231
No. of Authors : 3095
  Journal Paper

Paper Title
Generalized Regression Neural Networks For Reservoir Level Modeling

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


Author - Fatih Unes, Mustafa Demirci

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| Published on 2015-08-04
   
   
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