周倩倩,张〓茜,李阿婷,许苗苗.非平稳条件下基于贝叶斯网络的内涝风险评估和管理方法[J].水电能源科学,2018,36(10):80-83
非平稳条件下基于贝叶斯网络的内涝风险评估和管理方法
Bayesian Network based Urban Flood Risk Assessment and Management Framework under Nonstationary Conditions
  
DOI:
中文关键词:  非平稳条件  SWMM  内涝风险评估  贝叶斯网络
英文关键词:nonstationary conditions  SWMM  urban flood risk assessment  Bayes Server
基金项目:广东省公益研究与能力建设基金项目(2017A020219003);广东省自然科学基金项目(2014A030310121)
作者单位
周倩倩,张〓茜,李阿婷,许苗苗 广东工业大学 土木与交通工程学院 广东 广州 510006 
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中文摘要:
      受非平稳条件因素(如气候变化、城市化)的影响,近几年内涝灾害频发。以Z市某地块为研究区域,借助地理信息系统(ArcGIS)和暴雨雨洪管理模型(SWMM)实现研究区排水管网的水动力模拟。在不同降雨情境下,比较分析现状系统及不同改造方案下的系统溢流量等信息,计算内涝风险指数。采用贝叶斯网络分析工具(Bayes Server)对引发内涝风险的主要因素进行推理、识别和分析,进而构建内涝风险评估模型。该模型便于决策者根据设计需求,结合各因素的不确定性范围和发生概率值,综合选取最适合的改造措施,优化市政排水防涝规划,为市政基础设施建设提供了依据。
英文摘要:
      Urban flood has occurred more frequently due to nonstationary conditions (e.g. climate change and urbanization) in recent years. Using a case study in Z city, the Geographic Information System (ArcGIS) and the storm water management model (SWMM) were used to simulate the hydrodynamics of a drainage network. We compared the differences in system overloading in current and adaptation scenarios under different rainfall events, thus obtaining the index of flood risks. The Bayes Server was used to identify and analyze the main influencing factors of urban floods based on Bayesian Network theory. The established flood risk assessment framework is capable of selecting appropriate adaptation measures considering uncertainties and occurrences of probability associated with the main influencing factors in accordance to various design demands. The framework is thus useful in the design of urban drainage system and provides scientific support for the construction of municipal infrastructure.
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