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  

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

作　　者：叶陈雷 徐宗学 雷晓辉[2] 陈阳[3] 班春广 苏恒 YE Chenlei;XU Zongxue;LEI Xiaohui;CHEN Yang;BAN Chunguang;SU Heng(Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology,College of Water Sciences,Beijing Normal University,Beijing 100875;Department of Water Resources,China Institute of Water Resources and Hydropower Research,Beijing 100038;College of Resources and Civil Engineering,Northeastern University,Shenyang 110819)

机构地区：[1]北京师范大学水科学研究院城市水循环与海绵城市技术北京市重点实验室,北京100875 [2]中国水利水电科学研究院水资源研究所,北京100038 [3]东北大学资源与土木工程学院,沈阳110819

出　　处：《水力发电学报》2021年第10期81-94,共14页Journal of Hydroelectric Engineering

基　　金：国家自然科学基金(52079005)。

摘　　要：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。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.

关 键 词：SWMM模型 长短期记忆神经网络 模式识别法 遗传算法 Mann-Kendall趋势检验 

分 类 号：TU122[建筑科学—建筑理论]