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