Rainfall-runoff simulation is one of the most important processes in flood simulation, especially in the watersheds (Darake watershed) located upstream of large cities (populated city of Tehran) and exposed to floods. The study used SWAT and ANN models to simulate rainfall-runoff. The reasons for selecting these two models are A) SWAT is a physical and complex model that needs precipitation, temperature, wind speed, relative humidity, sundial, soil map, land use map and DEM to simulate, B) ANN model is a simple linear model that needs precipitation, runoff and temperature data. Moreover, SWAT model needs more time and cost than ANN model. In general, the purpose of the study is to evaluate the performance of two models with different structure in urban watershed. The results of this research showed that the performance of the artificial neural network is appropriate for predicting the maximum and minimum runoff values (R² = 0.75, NSE = 0.74), while the inputs of the model is appropriate and in areas where information is scarce is very appropriate, while the performance of the SWAT model is also appropriate and has very good performance (R² = 0.66, NSE = 0.65) in managerial and planning and economics studies, especially when there is no statistical station in the upper watershed. The SWAT model can properly simulate. It is better to use the SWAT model in the studies that are related to the flow trend.

降雨径流模拟是洪水模拟中最重要的过程之一，尤其是在大城市（德黑兰人口稠密的城市）上游且遭受洪水袭击的分水岭（达拉克分水岭）中。该研究使用了SWAT和ANN模型来模拟降雨径流。选择这两个模型的原因是：A）SWAT是一个物理和复杂的模型，需要降水，温度，风速，相对湿度，日d，土壤图，土地利用图和DEM进行模拟，B）ANN模型是简单的线性模型需要降水，径流和温度数据的模型。而且，SWAT模型比ANN模型需要更多的时间和成本。总的来说，该研究的目的是评估两种具有不同结构的模型在城市流域中的性能。²  = 0.75，NSE = 0.74），而模型的输入是适当的，在信息稀缺的地区是非常适当的，而SWAT模型的性能也是适当的，并且具有非常好的性能（R ²  = 0.66，NSE = 0.65），尤其是在上游流域没有统计站的情况下。特警模型可以正确模拟。最好在与流量趋势有关的研究中使用SWAT模型。