School of Environment, Harbin Institute of Technology, Harbin 150001, China.
Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China.
Int J Environ Res Public Health. 2023 Jan 31;20(3):2528. doi: 10.3390/ijerph20032528.
The complex formation mechanism and numerous influencing factors of urban waterlogging disasters make the identification of their risk an essential matter. This paper proposes a framework for identifying urban waterlogging risk that combines multi-source data fusion with hydrodynamics (MDF-H). The framework consists of a source data layer, a model parameter layer, and a calculation layer. Using multi-source data fusion technology, we processed urban meteorological information, geographic information, and municipal engineering information in a unified computation-oriented manner to form a deep fusion of a globalized multi-data layer. In conjunction with the hydrological analysis results, the irregular sub-catchment regions are divided and utilized as calculating containers for the localized runoff yield and flow concentration. Four categories of source data, meteorological data, topographic data, urban underlying surface data, and municipal and traffic data, with a total of 12 factors, are considered the model input variables to define a real-time and comprehensive runoff coefficient. The computational layer consists of three calculating levels: total study area, sub-catchment, and grid. The surface runoff inter-regional connectivity is realized at all levels of the urban road network when combined with hydrodynamic theory. A two-level drainage capacity assessment model is proposed based on the drainage pipe volume density. The final result is the extent and depth of waterlogging in the study area, and a real-time waterlogging distribution map is formed. It demonstrates a mathematical study and an effective simulation of the horizontal transition of rainfall into the surface runoff in a large-scale urban area. The proposed method was validated by the sudden rainstorm event in Futian District, Shenzhen, on 11 April 2019. The average accuracy for identifying waterlogging depth was greater than 95%. The MDF-H framework has the advantages of precise prediction, rapid calculation speed, and wide applicability to large-scale regions.
城市内涝灾害的复杂形成机制和众多影响因素使得对其风险的识别成为必要。本文提出了一种基于多源数据融合与水动力模型（MDF-H）的城市内涝风险识别框架。该框架由数据源层、模型参数层和计算层三部分组成。利用多源数据融合技术，以统一的面向计算的方式对城市气象信息、地理信息和市政工程信息进行处理，形成了全球化多数据层的深度融合。结合水文分析结果，对不规则的子汇水区进行划分，作为局部产流和汇流集中的计算容器。共考虑了气象数据、地形数据、城市下垫面数据和市政与交通数据等四类数据源，共 12 个因素作为模型输入变量，定义实时、全面的径流系数。计算层由总研究区、子汇水区和网格三个计算层次组成。结合水动力理论，实现了城市道路网络各级间的地表径流区域连通性。提出了基于排水管道体积密度的两级排水能力评估模型。最终的结果是研究区域的积水范围和深度，并形成实时的积水分布图。该方法实现了对大面积城市降雨向地表径流的水平转化的数学研究和有效模拟。利用 2019 年 4 月 11 日深圳福田区突发暴雨事件对所提出的方法进行了验证，识别积水深度的平均准确率大于 95%。MDF-H 框架具有预测精度高、计算速度快、适用于大规模区域的优点。
