The evaluation of surface water resources is a necessary input to solving water management problems, which includes finding a relationship between precipitation and runoff, and this relationship is a high degree of complexity. The rain of the most important factors that greatly effect on rivers discharge, and process to prediction of these flows must take this factor into account, and much of the attention and study, artificial neural networks and is considered one of the most modern methods in terms of accuracy results in linking these multiple factors and highly complex. In order to predict the runoff contained daily to Lake Dam Tishreen 16 in Latakia, the subject of our research, the application of different models of artificial neural networks (ANN), was the previous input flows and rain. Divided the data set for the period between (2006-2012) into two sets: training and test, has been processing the data before using them as inputs to the neural network using Discrete Wavelet Transform technique, to get rid of the maximum values and the values of zero, where t the analysis of time series at three levels of accuracy before they are used sub- series resulting as inputs to the Feed Forward ANN that depend back-propagation algorithm for training. The results indicated that with the structural neural network (1-2-6) Wavelet-ANN model, are the best in the representation of the characteristics studied and best able to predict runoff daily contained to Lake Dam Tishreen 16 for a day in advance, where he reached the correlation coefficient the root of the mean of squared-errors (R2 = 0.96, RMSE = 1.97m3 / sec), respectively.