Suitability of Different Neural Networks in Daily Reservoir Inflow Simulation

Climate change impact assessment studies often need models capable in simulating river streamflow on a daily time basis. In this study, different type of Artificial Neural Networks (ANNs) were analyzed in simulating the daily inflow into Taleghan reservoir in Iran. These types include: Elman Networks, Feed Forward Backpropagation Neural Networks with one (FFNN1) and tow (FFNN2) hidden layers, Focused Time Delay Networks (FTDN), Distributed Time Delay Networks (DTDN), General Regression Neural Network with standardized inputs (GRNN1) and with nonstandardized inputs (GRNN1), and Radial Basis Networks with standardized inputs (RBN1) and with non-standardized inputs (RBN2). An iterative algorithm was designed to assess different architecture of these models. Results revealed the potential of these models, specially Elman, RBN and GRNN, as suitable tools for simulating the daily reservoir inflow. Also, it was concluded that multiday averaging can improve the simulation results considerably.

Keywords:

Taleghan Reservoir , Daily Inflow , Artificial Neural Networks , Simulation , Comparison