Flood is one of the significant natural disasters, which is treated as one of the main global concerns which increased occurrence has led to an increase in mortality rates and economic losses. Various methods have been developed and proposed for the analysis of this natural disaster. Iran is among several countries in the world, which faces severe problems of flood each year particularly in urban catchments. The present study aims to utilize GIS spatial analysis functions, data from Hydrometric and Rain-Gauge stations, satellite images, and thematic data layers in the form of Artificial Neural Network Algorithm for prediction of discharge values and spatial modeling of floods in Kan River Basin located in Tehran province. An optimized artificial neural network of 7 inputs, including slope, slope curvature, flow accumulation, NDVI, geological units, soil type, and rainfall data along with eight, sixteen and one neurons for the first, second and output hidden layers, respectively, were designed and developed. The output of the neural network was discharge values in stations. According to Table .2 in the result section, ANN method has one of the highest correlation and lowest RMSE in flood modeling. Precision parameters such as R², RMSE and MAE were used to show the efficiency of the proposed model which yielded the values of 0.82, 0.18, and 0.13, respectively. The results obtained by the present study can be employed in future environmental planning at local scale as a means for improving the management of environmental risks and crises. The present study showed that an integrated utilization of GIS spatial analysis function with neural network algorithm is one of the high efficiency methods for predicting the potential of natural disasters such as floods.

洪水是重大自然灾害之一，被视为全球关注的主要问题之一，日益严重的自然灾害导致死亡率和经济损失增加。已经开发并提出了各种方法来分析这种自然灾害。伊朗是世界上几个国家之一，每年都面临着严重的洪灾问题，特别是在城市集水区。本研究旨在利用GIS空间分析功能，水文和雨量计站的数据，卫星图像以及以人工神经网络算法形式的专题数据层来预测位于坎Kan河流域的流量和洪水的空间模型在德黑兰省。经过优化的人工神经网络，包含7个输入，包括坡度，坡度曲率，流量累积，NDVI，地质单位，设计并开发了土壤类型和降雨数据，以及分别针对第一，第二和输出隐藏层的八个，十六个和一个神经元。神经网络的输出是站中的排放值。根据结果​​部分中的表.2，ANN方法在洪水建模中具有最高的相关性和最低的RMSE之一。精度参数，例如R^{如图2所示}，使用RMSE和MAE来显示所提出模型的效率，该模型分别产生0.82、0.18和0.13的值。通过本研究获得的结果可用于未来的地方环境规划，作为改善环境风险和危机管理的一种手段。本研究表明，将GIS空间分析功能与神经网络算法集成在一起是预测洪水等自然灾害潜在可能性的高效方法之一。