Paper Title
Generalized Regression Neural Networks For Reservoir Level Modeling

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