Flooding due to the increase of heavy rainfall caused even larger damage in metropolitan areas. Therefore, numerical simulation and probabilistic models have been used for flood prediction, but the methodologies for real-time flood prediction by drainage district in metropolitan areas are still not sufficient. In this study, a flood scenario database was established by using one- and two-dimensional hydraulic analysis models to propose a realtime urban flood prediction method by drainage districts in metropolitan areas. Flood prediction models were constructed for each drainage district through the Nonlinear Auto-Regressive with eXogenous inputs and Self-Organizing Map (NARX-SOM). Suggested prediction model is a data-driven model because it is based on flood database which composed with diverse flood simulation results. To evaluate the predictive capacity of the models, flood prediction was performed for the actual heavy rainfall in 2010 and 2011 that caused severe flooding in Seoul, Republic of Korea. Flood prediction models for a total of 24 drainage districts were constructed, and it was found that the goodness of fit on the flood area ranged from 68.7 to 89.7%. In terms of the expected inundation map, the predictive power was found to be high when the SOM result with 5 × 5 dimension was mainly used. Through this study, it was possible to identify the predictive capability of the NARX-SOM flood prediction algorithm. The time for inundation map prediction for each area was within two minutes, but the one- and two-dimensional flood simulation usually takes 60–80 minutes. Moreover, when the calculated goodness of fit was examined, the proposed method was found to be a practical methodology that can be helpful in improving flood response capabilities.

由于暴雨的增加而造成的洪水在大都市地区造成了更大的破坏。因此，已经将数值模拟和概率模型用于洪水预报，但是大都市地区的流域实时洪水预报的方法仍然不够。在这项研究中，通过使用一维和二维水力分析模型建立洪水情景数据库，提出了大城市流域实时城市洪水预报方法。通过带有外源输入的非线性自回归和自组织图（NARX-SOM），为每个流域构建了洪水预测模型。建议的预测模型是一种数据驱动的模型，因为它基于洪水数据库，该数据库包含各种洪水模拟结果。为了评估模型的预测能力，对2010年和2011年导致韩国首尔发生严重洪灾的实际强降雨进行了洪水预测。建立了总共24个流域的洪水预报模型，发现洪水区的拟合优度在68.7％至89.7％之间。根据预期的淹没图，当主要使用5×5维的SOM结果时，发现预测能力很高。通过这项研究，有可能确定NARX-SOM洪水预测算法的预测能力。每个区域的淹没图预测时间在两分钟之内，但是一维和二维洪水模拟通常需要60-80分钟。此外，在检查计算出的拟合优度时，