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作 者:张朝阳[1] 方宏远[1,2] 狄丹阳 贾浩 白渝[1] ZHANG Zhao-yang;FANG Hong-yuan;DI Dan-yang;JIA Hao;BAI Yu(School of Water Conservancy&Transportation,Zhengzhou University,Zhengzhou 450001,China;Yellow River Laboratory,Zhengzhou 450018,China)
机构地区:[1]郑州大学水利与交通学院,河南郑州450001 [2]黄河实验室,河南郑州450018
出 处:《中国市政工程》2025年第1期15-19,142,共6页China Municipal Engineering
基 金:“十四五”国家重点研发计划(2022YFC3801000);国家自然科学基金项目(42477172);河南省高校科技创新人才支持计划(24HASTIT026);郑州大学研究生自主创新项目(20240308)。
摘 要:近年来,强降雨事件所引发的城市河道漫堤致使内涝灾害愈发难以应对,针对此类情况,开展考虑河道分洪的灰绿耦合设施多目标优化研究。研究基于InfoWorks ICM模型分析郑州市金水河区域内涝成因并提出相应的灰绿改造方案,利用改进的NSGA-Ⅱ算法和TOPSIS法得到灰绿耦合设施的最优改造方案,并分析其对城市内涝灾害的削减效果。得到主要结论如下:内涝成因主要为不透水用地类型占比较高、管道排水能力不足、金水河河道漫堤;考虑河道分洪的灰绿耦合设施改造方案能够有效减少城市内涝灾害,50年降雨情况下减弱效果最突出,积水深度减少0.41 m、淹没范围减少14.4%,但改造方案对于极端强降雨所引发的内涝灾害削减效果一般。In recent years,urban flash floods caused by extreme rainfall events have become increasingly difficult to manage due to river overflows in cities.To address this issue,this study conducts a multi-objective optimization research on grey-green coupling facilities considering river flood diversion.Based on the InfoWorks ICM model,the study analyzes the causes of urban flooding in the Jinshui River area of Zhengzhou and proposes corresponding grey-green retrofitting schemes.By employing an improved NSGA-II algorithm and the TOPSIS method,the optimal retrofitting scheme for grey-green coupling facilities is obtained,and its effectiveness in reducing urban flooding disasters is analyzed.The main conclusions are as follows:the primary causes of flooding are the high proportion of impermeable land use types,insufficient pipeline drainage capacity,and river overflows in the Jinshui River area.The grey-green coupling facility retrofitting scheme considering river flood diversion can effectively reduce urban flooding disasters.Under the 50-year return period rainfall scenario,the reduction effect is most prominent,with a decrease of 0.41 m in water depth and a 14.4%reduction in flooded area.However,the retrofitting scheme has a moderate effect on reducing flooding disasters caused by extremely heavy rainfall events.
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