Reservoir Storage Simulation Using Artificial Neural Network Models –Lar Dam

Artificial intelligence has been developed recently due to its efficiency in various aspect of science. For instance, there have been lots of improvements in the artificial neural networks which are used also as a predictor and simulator in order to evaluate the performance of water resource systems. Artificial neural networks are working based on the past events observation and establishing empirical relations among them. Furthermore, much attention has been considered today for the optimalmanagement of water resources forecasting system components (WRFSC). Due to importance of WRFSC, a statistical model has been developed in this paper which predicts the storage volume of reservoirs with the means of different type of networks such as artificial neural networks, dynamic neural networks, etc.; the results of the examination of models have been illustrated and the best fitted model has been selected. Lar dam has been used as a case study in this paper which is located 35kilometers far from Rude Hen in order to select the most efficient among variousneural network models. Lar dam has an important role in supply water demand of Tehran metropolitan. In order to design a model which is estimating the most realistic view of future conditions, different models have been studied and compared. The results of this paper indicate storage volume of reservoir which is simulated by artificial neural network could be used in future performance policies of dams

کلیدواژه ها:

Reservoirs Storage ، Artificial Neural Networks ، WRFSC ، Water Supply