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【目的】针对城市内部河网对闸泵控制规则响应相对滞后的问题，急需对现有河网实时控制规则进行优化，以减轻暴雨期间河网对排水管网的顶托，进而降低洪涝风险。【方法】首先，采用InfoWorks ICM构建洪涝模型识别了不同设计暴雨下的顶托排水口，并定量分析了顶托排水口现状和最大排水能力下的陆面淹没响应。然后，基于AGNES和K-Medoids算法构建了双层聚类模型，根据顶托排水口的特征确定了河网调控点。最后，以河网调控点为调控参考对象优化了河网实时控制规则，通过观测暴雨事件评估了所提方法的性能。以福州市仓山区双向感潮片区为案例进行分析。【结果】结果显示：河网顶托的排水口比例是23.73%,确定了3个河网调控点。“苏迪罗”超强台风暴雨下，对比原有规则，优化规则下河道断面水头降低了3.51%,溢流雨水井数量减少了1.53%,陆面淹没面积和深度分别减少了6.18%和9.25%。【结论】识别顶托排水口可初步揭示洪涝风险区，计算陆面淹没响应可为确定河网调控点和优化河网实时控制规则提供可靠依据；通过对比模拟观测暴雨事件，证实了优化规则对降低河道断面水头的有效性和优越性，表明了更新闸泵调控参考对象是提高城市内部河网响应速度、缓解洪涝风险的关键途径；所提方法为优化河网运行、提升城市洪涝管理能力提供参考。
Abstract：[Objective]To address the delayed response of urban river networks to sluice and pump control rules, existing real-time control rules should be optimized to mitigate the backwater effect on drainage networks during storm events, thereby reducing flood risks.[Methods]First, a flood model was established using InfoWorks ICM to identify backwater-affected drainage outlets under different design storm events, followed by a quantitative analysis of surface inundation responses under both existing conditions and maximum drainage capacity. Then, a two-layer clustering model integrating AGNES and K-Medoids algorithms was established to determine river network control points based on the characteristics of backwater-affected drainage outlets. Finally, using these control points as control references, real-time control rules were optimized, and the performance of the proposed method was evaluated through observed storm events. A bidirectional tidal-influenced area in Cangshan District, Fuzhou City, was selected as a case study.[Results]The result showed that 23.73% of drainage outlets experienced backwater, and three river network control points were determined. Under the Super Typhoon Soudelor storm event, the optimized rules reduced the water head at river cross-sections by 3.51%, decreased the number of overflowing stormwater inlets by 1.53%, and lowered the surface inundation area and depth by 6.18% and 9.25%, respectively, compared to the original rules.[Conclusion]Identifying backwater-affected drainage outlets helps preliminarily locate flood-prone areas, and analyzing surface inundation responses provides a reliable basis for determining river network control points and optimizing real-time control rules. The comparative simulation of observed storm events verifies the effectiveness and advantages of the optimized rules in reducing river cross-section water head. Furthermore, it shows that updating sluice and pump control references is crucial for enhancing the responsiveness of urban river networks and mitigating flood risks. The proposed method provides a practical reference for optimizing river network operations and improving urban flood management.
[1] 张金良，罗秋实，王冰洁，等.城市极端暴雨洪涝灾害成因及对策研究进展[J].水资源保护，2024,40(1):6-15.ZHANG J L,LUO Q S,WANG B J,et al.Research progress on causes and countermeasures for extreme rainstorm-induced urban flood disasters[J].Water Resources Protection,2024,40(1):6-15.
[2] 张建云，宋晓猛，王国庆，等.变化环境下城市水文学的发展与挑战：I.城市水文效应[J].水科学进展，2014,25(4):594-605.ZHANG J Y,SONG X M,WANG G Q,et al.Development and challenges of urban hydrology in a changing environment:I:Hydrological response to urbanization[J].Advances in Water Science,2014,25(4):594-605.
[3] 舒心怡，徐宗学，叶陈雷，等.城市化下产汇流参数不确定性分析及洪涝模拟：以晋城市金村区为例[J].水力发电学报，2023,42(5):53-66.SHU X Y,XU Z X,YE C L,et al.Uncertainty analysis of runoff generation and concentration parameters under urbanization and simulation of flooding and waterlogging.Case study of Jincun District,Jincheng[J].Journal of Hydroelectric Engineering,2023,42(5):53-66.
[4] WANG P,LI Y B,ZHANG Y H.An urban system perspective on urban flood resilience using SEM:Evidence from Nanjing city,China[J].Natural Hazards,2021,109(3):2575-2599.
[5] 徐宗学，陈浩，任梅芳，等.中国城市洪涝致灾机理与风险评估研究进展[J].水科学进展，2020,31(5):713-724.XU Z X,CHEN H,REN M F,et al.Progress on disaster mechanism and risk assessment of urban flood/waterlogging disasters in China[J].Advances in Water Science,2020,31(5):713-724.
[6] 吕佳豪，侯精明，李东来，等.城市排水管网排口水位变化对城市洪涝过程的影响[J].给水排水，2023,49(5):140-149.LYU J H,HOU J M,LI D L,et al.Impacts of variation of drainage pipe network outlet water level on urban flood inundation[J].Water & Wastewater Engineering,2023,49(5):140-149.
[7] 吴端炜.基于SWMM的东南沿海感潮河网城区LID雨水控制技术研究[D].福州：福州大学，2021.WU D W.Research on LID Stormwater Control Technology of the Tidal River Network Urban Area in Southeast Coast Based on SWMM[D].Fuzhou:Fuzhou University,2021.
[8] 王小杰，夏军强，周美蓉，等.降雨和外江水位变化对城市洪涝的联合影响分析：以武汉市为例[J].应用基础与工程科学学报，2023,31(5):1061-1074.WANG X J,XIA J Q,ZHOU M R,et al.Joint impact of rainfall and river water level on urban flooding:A case study of Wuhan City[J].Journal of Basic Science and Engineering,2023,31(5):1061-1074.
[9] 周紫蕊，廖卫红，雷晓辉，等.基于 InfoWorks ICM 的城市河网调控与分析：以福州市仓山区龙津阳岐片区为例[J].南水北调与水利科技(中英文),2023,21(4):770-779.ZHOU Z R,LIAO W H,LEI X H,et al.InfoWorks ICM-based urban river network regulation and analysis:A case study of Longjin Yangqi area in Cangshan District,Fuzhou City[J].South-to-North Water Transfers and Water Science & Technology,2023,21(4):770-779.
[10] BEENEKEN T,ERBE V,MESSMER A,et al.Real time control (RTC) of urban drainage systems:A discussion of the additional efforts compared to conventionally operated systems[J].Urban Water Journal,2013,10(5):293-299.
[11] TIAN W C,LIAO Z L,ZHI G Z,et al.Combined sewer overflow and flooding mitigation through a reliable real-time control based on multi-reinforcement learning and model predictive control[J].Water Resources Research,2022,58(7):e2021WR030703.
[12] 徐存东，任子豪，黄嵩，等.基于动态规划理论的MIKE21河网排水方案优化研究[J].环境工程，2023,41(1):79-86.XU C D,REN Z H,HUANG S,et al.Optimization of mike21 river network drainage scheme based on dynamic planning theory[J].Environmental Engineering,2023,41(1):79-86.
[13] 全星.广州市某河涌流域城市排水系统实时控制模型研究[D].广州：华南理工大学，2020.QUAN X.Study on Real-time Control Model of Urban Drainage System in One River Catchment of Guangzhou City[D].Guangzhou:South China University of Technology,2020.
[14] YE C L,XU Z X,LEI X H,et al.Assessment of the impact of urban water system scheduling on urban flooding by using coupled hydrological and hydrodynamic model in Fuzhou City,China[J].Journal of Environmental Management,2022,321:115935.
[15] BARJAS BLANCO T,WILLEMS P,DE MOOR B,et al.Flooding prevention of the demer river using model predictive control[J].IFAC Proceedings Volumes,2008,41(2):3629-3634.
[16] FENG W W,LEI X H,JIANG Y Z,et al.Coupling model predictive control and rules-based control for real-time control of urban river systems[J].Journal of Hydrology,2024,636:131228.
[17] VERMUYTEN E,MEERT P,WOLFS V,et al.Combining model predictive control with a reduced genetic algorithm for real-time flood control[J].Journal of Water Resources Planning and Management,2018,144(2):04017083.
[18] 吴文鑫，高杰，于会来，等.融合凝聚层次聚类和互相关函数的城市排水管网监测点优化布置研究[J].水利水电技术(中英文),2025,56(6):101-110.WU W X,GAO J,YU H L,et al.Optimizing the layout of urban drainage pipeline monitoring points using agglomerative hierarchical clustering and cross-correlation functions[J].Water Resources and Hydropower Engineering,2025,56(6):101-110.
[19] 李红艳，常子峰，史文韬，等.基于高程邻域信息的FCM算法管网漏损控制策略[J].控制与决策，2025,40(3):946-954.LI H Y,CHANG Z F,SHI W T,et al.Network leakage control strategy based on high range neighborhood information based on FCM algorithm[J].Control and Decision,2025,40(3):946-954.
[20] LIU L,LI X,XIA G Y,et al.Spatial fuzzy clustering approach to characterize flood risk in urban storm water drainage systems[J].Natural Hazards,2016,83(3):1469-1483.
[21] XU H S,MA C,LIAN J J,et al.Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou,China[J].Journal of Hydrology,2018,563:975-986.
[22] 吴礼福，姬广慎，胡秋岑.强混响环境下基于K-medoids特征聚类的话者计数[J].南京大学学报(自然科学),2021,57(5):875-880.WU L F,JI G S,HU Q C.Speaker counting in strong reverberant environments based on K-medoids clustering coherence features[J].Journal of Nanjing University (Natural Sciences),2021,57(5):875-880.
[23] ALLEN G I,GAN L Q,ZHENG L L.Interpretable machine learning for discovery:Statistical challenges and opportunities[J].Annual Review of Statistics and Its Application,2024,11:97-121.
[24] CHAKRABORTY D,BASAGAOGLU H,WINTERLE J.Interpretable vs.noninterpretable machine learning models for data-driven hydro-climatological process modeling[J].Expert Systems with Applications,2021,170:114498.
[25] GUO Z F,LEITAO J P,SIMOES N E,et al.Data-driven flood emulation:Speeding up urban flood predictions by deep convolutional neural networks[J].Journal of Flood Risk Management,2021,14(1):e12684.
[26] BISHT D S,CHATTERJEE C,KALAKOTI S,et al.Modeling urban floods and drainage using SWMM and MIKE URBAN:A case study[J].Natural Hazards,2016,84(2):749-776.
[27] LI D Y,MARSHALL L,LIANG Z M,et al.Bayesian LSTM with stochastic variational inference for estimating model uncertainty in process-based hydrological models[J].Water Resources Research,2021,57(9):e2021WR029772.
[28] PATRO S,CHATTERJEE C,MOHANTY S,et al.Flood inundation modeling using MIKE FLOOD and remote sensing data[J].Journal of the Indian Society of Remote Sensing,2009,37(1):107-118.
[29] 张海丽，贺新春，邓家泉.基于闸泵联控的感潮河网区水动力水质调控[J].长江科学院院报，2019,36(8):36-41.ZHANG H L,HE X C,DENG J Q.Regulation of hydrodynamics and water quality in tidal river network based on gate-pump joint operation[J].Journal of Yangtze River Scientific Research Institute,2019,36(8):36-41.
[30] MIGNOT E,DEWALS B.Hydraulic modelling of inland urban flooding:Recent advances[J].Journal of Hydrology,2022,609:127763.
[31] 官保君，侯精明，李东来，等.小区尺度雨洪过程模拟及径流分配特性研究[J].水力发电学报，2024,43(3):24-34.GUAN B J,HOU J M,LI D L,et al.Study on simulations of rainfall-flood processes and runoff allocation characteristics on community scale[J].Journal of Hydroelectric Engineering,2024,43(3):24-34.
[32] 陈笑凯.InfoWorks ICM模型在泰州排水防涝规划中的应用研究[D].南昌：南昌大学，2023.CHEN X K.Application of InfoWorks ICM Model in Drainage and Flood Control Planning of Taizhou city[D].Nanchang,Nanchang University,2023.
[33] 张凤荔，周洪川，张俊娇，等.基于改进凝聚层次聚类的协议分类算法[J].计算机工程与科学，2017,39(4):796-803.ZHANG F L,ZHOU H C,ZHANG J J,et al.A protocol classification algorithm based on improved AGNES[J].Computer Engineering and Science,2017,39(4):796-803.
[34] 郭松，郭广礼，李怀展，等.基于主成分层次聚类模型的采空塌陷场地稳定性评价[J].中国地质灾害与防治学报，2020,31(6):116-121.GUO S,GUO G L,LI H Z,et al.Goaf-collapse sites stability evaluation based on principal component hierarchical clustering model[J].The Chinese Journal of Geological Hazard and Control,2020,31(6):116-121.
[35] 陈苏豫，顾亦然，张腾飞.基于DLT-Kmedoids算法的用电负荷聚类分析[J].计算机技术与发展，2024,34(4):205-211.CHEN S Y,GU Y R,ZHANG T F.Power load clustering analysis based on DLT-kmedoids[J].Computer Technology and Development,2024,34(4):205-211.
[36] USHAKOV A V,VASILYEV I.Near-optimal large-scale k-medoids clustering[J].Information Sciences,2021,545:344-362.
[37] 谢娟英，周颖，王明钊，等.聚类有效性评价新指标[J].智能系统学报，2017,12(6):873-882.XIE J Y,ZHOU Y,WANG M Z,et al.New criteria for evaluating the validity of clustering[J].CAAI Transactions on Intelligent Systems,2017,12(6):873-882.
[38] 傅立伟，武森.基于属性值集中度的分类数据聚类有效性内部评价指标[J].工程科学学报，2019,41(5):682-693.FU L W,WU S.A new internal clustering validation index for categorical data based on concentration of attribute values[J].Chinese Journal of Engineering,2019,41(5):682-693.
[39] 苏丰睿，穆伟伟，赵宣茗，等.一种划分聚类k值与中心初始化的改进方法[J].计算机工程，2023,49(11):85-93.SU F R,MU W W,ZHAO X M,et al.Improved method for partitioning clustering k-values and center initialization[J].Computer Engineering,2023,49(11):85-93.
[40] 孙家政，姜红，刘新磊，等.差分拉曼光谱技术结合K-means聚类法对牙膏的快速分类[J].理化检验-化学分册，2022,58(1):84-89.SUN J Z,JIANG H,LIU X L,et al.Rapid classification of toothpaste by differential Raman spectroscopy combined with K-means clustering method[J].Physical Testing and Chemical Analysis Part B (Chemical Analysis),2022,58(1):84-89.
[41] 王建仁，马鑫，段刚龙.改进的K-means聚类k值选择算法[J].计算机工程与应用，2019,55(8):27-33.WANG J R,MA X,DUAN G L.Improved K-means clustering k-value selection algorithm[J].Computer Engineering and Applications,2019,55(8):27-33.
[42] 徐晨辉，黄国鲜，夏波，等.湘江下游流域-河网-枢纽的水文水动力模拟[J].水力发电学报，2022,41(11):21-33.XU C H,HUANG G X,XIA B,et al.Integrated modelling of hydrological and hydrodynamic processes in watershed-rivers and navigation-hydropower project system of lower Xiangjiang River[J].Journal of Hydroelectric Engineering,2022,41(11):21-33.
[43] 黄国如，罗海婉，陈文杰，等.广州东濠涌流域城市洪涝灾害情景模拟与风险评估[J].水科学进展，2019,30(5):643-652.HUANG G R,LUO H W,CHEN W J,et al.Scenario simulation and risk assessment of urban flood in Donghaochong basin,Guangzhou[J].Advances in Water Science,2019,30(5):643-652.
[44] CHEN T L,CHEN L,SHAO Z Y,et al.Enhanced resilience in urban stormwater management through model predictive control and optimal layout schemes under extreme rainfall events[J].Journal of Environmental Management,2024,366:121767.
[45] YE S J,WANG C,WANG Y L,et al.Real-time model predictive control study of Run-of-river hydropower plants with data-driven and physics-based coupled model[J].Journal of Hydrology,2023,617:128942.
基本信息:
DOI：10.13928/j.cnki.wrahe.2025.12.001
中图分类号:TU992
引用信息:
[1]高祎,张晋,廖卫红,等.基于AGNES和K-Medoids算法的河网实时控制规则优化方法[J].水利水电技术(中英文),2025,56(12):1-14.DOI:10.13928/j.cnki.wrahe.2025.12.001.
基金信息:
国家重点研发计划(2022YFC3800102); 水利部重大科技项目(SKS-2022014)