A flood hazard rating prediction model was developed that is based on a long short-term memory (LSTM) neural network and random forest. The target area was Samseong District in Seoul, which has a history of severe flooding. The Storm Water Management Model was used to generate training data for the LSTM model to predict the total overflow as the rainfall input data. Two-dimensional numerical analysis was performed to calculate inundation and flow velocity maps for training the random forest, which was used to generate a map of the predicted flood hazard rating of grid units given the total accumulative overflow of the target area. To confirm the goodness of fit, the proposed model was used to predict a flood hazard rating map for a rainfall event observed on July 27, 2011. The prediction accuracy for the flood hazard rating of each grid was 99.86% when the debris factor was considered and 99.99% when the debris factor was not considered.

建立了基于长期短期记忆（LSTM）神经网络和随机森林的洪水灾害等级预测模型。目标地区是首尔的三城地区，该地区曾发生过严重洪灾。雨水管理模型用于为LSTM模型生成训练数据，以预测总溢流作为降雨输入数据。进行了二维数值分析，以计算用于训练随机森林的淹没图和流速图，该图用于在给定目标区域的总累积溢流的情况下生成网格单元的预测洪灾危害等级图。为了确认拟合的良好性，所提出的模型用于预测2011年7月27日观测到的降雨事件的洪水灾害等级地图。每个网格的洪水灾害等级的预测准确性为99。