Making estimation of river flow with hydrological and methodological data of the past period and using in water resources project studies have been used for a long time in many studies in our country and in the world. In addition to this, along with frequent drought problems in recent years, it is also important to store and use the water in the reservoirs correctly. This study aims to research how two separate Artificial-Neural-Network-Functions, which are generated based on the study of neural networks in the human brain, can be used in areas with a certain reservoir capacity such as dams or ponds and to provide an example of the most appropriate ANN model for predicting the level changes that will occur depending on the next years. In this context, two separate functions have been evaluated. These are the Levenberg-Marquardt-(LM) training function and Gradient-Descent-with-Momentum(GDM) training functions. Here, instead of the best results obtained from the models, the best three model outputs were evaluated and the models were considered from a wide frame. Gökçe dam basin in Marmara region was selected as the Model Study Area. For the period of December 31, 2019 - January 1, 2000, the monthly average data prepared with daily data from DSI and different architectural ANN training models were used to forecast the monthly basin capacities for 2019. It can be stated that LM Training models created in terms of Basin reservoir level estimates give results closer to the measured value.