摘要
城市汛期中内涝灾害发生已经成为常态,基于系统的内涝风险评估结果,进行科学的空间格局优化设计,有利于城市规划与管理决策。以典型的内涝易发区滁州市中心城区为例,通过筛选内涝风险驱动因子,引入贝叶斯网络模型,依据条件概率图表和熵差法分析结果,确定关键变量条件状态子集,通过设置空间格局优化情景得到内涝风险一级优化区、二级优化区和不宜优化区。结果表明:研究区内涝风险的空间分布特征为中部核心区高于周围边缘区域,沿清流河往外逐步降低;{Veg=1;Riv=1}为关键变量最优状态子集,{Veg=1;Riv=3}为关键变量次优状态子集,{Veg=3;Riv=3}为关键变量不优状态子集;一级、二级优化区面积分别为5.14 km^(2)、4.79 km^(2),并提出需合理布局绿地系统和建设防涝工程的改善措施。
Waterlogging disaster has become a normal occurrence in urban flood season.Based on systematic waterlogging risk assessment results,scientific spatial pattern optimization design is conducive to urban planning and management decisions.Taking Chuzhou central city,one of the representative waterlogging prone area,as a case study area,we selecte the driving factors of waterlogging risk,construct Bayesian network,and use the results of conditional probability and entropy difference method to identify conditional state subsets of key variables.Finally,we set spatial optimization scenarios to obtain the spatial distribution of optimization area,secondary optimization area and unfavorable optimization area.The results show that the spatial distribution of waterlogging risk is higher in the central core area than in the surrounding marginal areas,and gradually decreases along the Qingliu River.The conditional state subset{Veg=1;Riv=1}is the optimal state subset of key variables,{Veg=1;Riv=3}is the suboptimal subset of key variables,and{Veg=3;RIV=3}is the unoptimal state subset of key variables.The area of the first and second level optimized areas is 5.14 km^(2) and 4.79km^(2) respectively,and the improvement measures of reasonable distribution of green space system and construction of waterlogging prevention projects are proposed.
作者
杨海峰
翟国方
葛懿夫
钟光淳
YANG Haifeng;ZHAI Guofang;GE Yifu;ZHONG Guangchun(School of Architecture and Planning,Nanjing University,Nanjing 210093,China)
出处
《灾害学》
CSCD
北大核心
2021年第4期181-187,共7页
Journal of Catastrophology
基金
日本学术振兴会项目(18K03022)。
关键词
内涝风险
贝叶斯网络
空间格局优化
驱动因子
滁州市中心城区
waterlogging risk
Bayesian belief networks
spatial pattern optimization
driving factors
Chuzhou central city
