Due to water shortage in country, more accurate estimate of water reserve can be one of the most important guidelines on the optimal management of water resource and cycle for development of water productivity efficiency. Therefore, using artificial neural network techniques the water supply of 174 fallen trees from different species was simulated. From any part of each bole, components of constant volume were extracted and placed in 105ºC to be oven-dried to measure specific drought index and wood density. Three input layers of diameter at breast height, height and specific wood density were used to simulate the response variable. The method of trial and test were used for neural network topology architecture. The results showed that the use of only diameter as input layer based on the validation indices explained 65% of variance of test of data. Using the three layers in the neural network, optimized output including function of Tan-sigmoid in the designed architecture with the number of 15 neurons demonstrated the highest accuracy (R²=0/92, MSE= 0/001, RMSE=81/08). In order to save the costs and manpower and to avoid a destructive method, the optimized output in the form of black box has the wide applicability to predict the water reserve in the mixed-beech forests to manage water cycle in the studied ecosystem.