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Effectiveness of urban distributed runoff model for discharge and water depth calculation in urban drainage pipe networks
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作者 Yang Zhou Yi-ling Leng +3 位作者 Peng-yu Wang Shang-hong Zhang Yu-long Zhu Yu-jun Yi 《Journal of Hydrodynamics》 SCIE EI CSCD 2024年第3期582-591,共10页
Effective urban land-use re-planning and the strategic arrangement of drainage pipe networks can significantly enhance urban flood defense capacity.Aimed at reducing the potential risks of urban flooding,this paper pr... Effective urban land-use re-planning and the strategic arrangement of drainage pipe networks can significantly enhance urban flood defense capacity.Aimed at reducing the potential risks of urban flooding,this paper presents a straightforward and efficient approach to an urban distributed runoff model(UDRM).The model is developed to quantify the discharge and water depth within urban drainage pipe networks under varying rainfall intensities and land-use scenarios.The Nash efficiency coefficient of UDRM exceeds 0.9,which indicates its high computational efficiency and potential benefit in predicting urban flooding.The prediction of drainage conditions under both current and re-planned land-use types is achieved by adopting different flood recurrence intervals.The findings reveal that the re-planned land-use strategies could effectively diminish flood risk upstream of the drainage pipe network across 20-year and 50-year flood recurrence intervals.However,in the case of extreme rainfall events(a 100-year flood recurrence),the re-planned land-use approach fell short of fulfilling the requirements necessary for flood disaster mitigation.In these instances,the adoption of larger-diameter drainage pipes becomes an essential requisite to satisfy drainage needs.Accordingly,the proposed UDRM effectively combines land-use information with pipeline data to give practical suggestions for pipeline modification and land-use optimization to combat urban floods.Therefore,this methodology warrants further promotion in the field of urban re-planning. 展开更多
关键词 Stormwater runoff water depth urban distributed runoff model urban drainage pipe networks urban land-use re-planning
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RS-SVM Machine Learning Approach Driven by Case Data for Selecting Urban Drainage Network Restoration Scheme
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作者 Li Jiang Zheng Geng +4 位作者 Dongxiao Gu Shuai Guo Rongmin Huang Haoke Cheng Kaixuan Zhu 《Data Intelligence》 EI 2023年第2期413-437,共25页
Urban drainage pipe network is the backbone of urban drainage,flood control and water pollution prevention,and is also an essential symbol to measure the level of urban modernization.A large number of underground drai... Urban drainage pipe network is the backbone of urban drainage,flood control and water pollution prevention,and is also an essential symbol to measure the level of urban modernization.A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age.The repair of drainage pipe networks has attracted extensive attention from all walks of life.Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019,various provinces in the Yangtze River Basin,such as Anhui,Jiangxi and Hunan,have extensively carried out PPp projects for urban pipeline restoration,in order to improve the quality and efficiency of sewage treatment.Based on the management practice of urban pipe network restoration project in Wuhu City,Anhui Province,this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode,and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine.The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice.The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes,and its accuracy can reach 90%.The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects. 展开更多
关键词 drainage pipe network Machine learning Rough set Multilevel SVM Restoration scheme
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