城市社区尺度降雨径流快速模拟——以福州市一排水小区为例

随着城市化进程的不断加快，我国城市正面临着越来越严峻的洪涝问题。本文在社区尺度上构建雨洪管理模型（SWMM），使用遗传算法率定SWMM模型参数；在对研究区降雨分析的基础上，采用模糊识别法筛选出最具代表性的两种雨型；基于不同的降雨情景与SWMM模拟值组成的数据集，建立长短期记忆神经网络（LSTM）模型模拟研究区降雨径流关系，并使用不同工况评估了LSTM模型效果。结果表明，LSTM模型对降雨径流的模拟与SWMM模型基本吻合，而其对洪峰流量的拟合略有偏差。在较小降雨下，LSTM模型模拟洪峰流量较SWMM输出结果偏小；在较大降雨下，模拟结果偏大；在中等降雨时，模拟效果最好。此外，50个隐含层单元的拟合效果更好，但同时更多的隐含层单元对洪峰流量拟合效果更好。

Rapid simulations of storm water runoff in urban community scale. Case study of a community compound in Fuzhou City

With the rapid development of urbanization, most cities in China are faced with increasingly severe flood problems. In this paper, a storm water management model (SWMM) is constructed on a community compound scale, and its parameters are determined using the genetic algorithm. Then, two types of most representative rainstorms are selected using a fuzzy identification method based on an analysis of precipitation over the study area. Finally, a long short-term memory (LSTM) model is developed for this area based on the dataset of its different rainfall data and SWMM simulations, a relationship between its rainfall and runoff is obtained, and the LSTM model is evaluated against different conditions. The results show that LSTM simulations agree roughly with those of SWMM, but its peak flood fitting is slightly deviated. Compared with SWMM simulations, LSTM simulated peak floods are smaller for small rainfall and greater for greater rainfall, and LSTM performs the best for medium rainfall. The peak flow fitting is better with 50 hidden layer elements, but more elements will produce an even better effect on the peak flow.