Comparative Study of Artificial Neural Networks in Water Reservoirs Storage Analysis – Case Study

In the recent years, there have been lots of improvements in the artificial intelligence areas which artificial neural networks is one of those. It works based on the past events empirical relations which theyhave been occurred. Nowadays using this network become more common among scientists and engineers due to its predictions ability and there are different type of these networks which they have typical usages. On the other hand, much attention has been considered today for the optimal management of water resources forecasting system components (WRFSC). Due to importance of WRFSC, we have developed astatistical model which it predicts the volume stored in reservoirs by using different type of networks such as artificial neural networks, recursive neural networks, dynamic neural networks and other neural networks; the result of the examination of models have been illustrate and the best fitted model had beenselected. We have chosen the Lar dam which it is located 35 kilometers far from Rude Hen to examine our model. Lar dam has an important role for water supply needs in Tehran. To design a model which helps for estimating of scientific and engineering situations, we have studied and compared different models. The results of our modeling indicate a functional model simulation as a tool in water management scenarios of dam reservoirs

Comparative Study of Artificial Neural Networks in Water Reservoirs Storage Analysis – Case Study Keywords:

Reservoirs Storage , Artificial Neural Networks , WRFSC , Water Supply