Zhifeng Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zhifeng Li has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jianchun Zheng
Beijing Research Center of Urban Systems Engineering, Beijing 100035, China
1 shared publication - Lixin Wu
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
1 shared publication - Miaole Hou
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
1 shared publication - Wei Zhu
Beijing Research Center of Urban Systems Engineering, Beijing 100035, China
1 shared publication - Yizhou Yang
Institute for Geo-Informatics and Digital Mine Research, Northeastern University, Shenyang 110819, China
1 shared publication
Latest Publications
- Open Access
A New Method for Urban Storm Flood Inundation Simulation with Fine CD-TIN Surface
Urban storm inundation, which frequently has dramatic impacts on city safety and social life, is an emergent and difficult issue. Due to the complexity of urban surfaces and the variety of spatial modeling elements, the lack of detailed hydrological data and accurate urban surface models compromise the study and implementation of urban storm inundation simulations. This paper introduces a Constrained Delaunay Triangular Irregular Network (CD-TIN) to model fine urban surfaces (based on detailed ground sampling data) and subsequently employs a depression division method that refers to Fine Constrained Features (FCFs) to construct computational urban water depressions. Storm-runoff yield is placed through mass conservation to calculate the volume of rainfall, runoff and drainage. The water confluences between neighboring depressions are provided when the water level exceeds the outlet of a certain depression. Numerical solutions achieved through a dichotomy are introduced to obtain the water level. Therefore, the continuous inundation process can be divided into different time intervals to obtain a series of inundation scenarios. The main campus of Beijing Normal University (BNU) was used as a case study to simulate the “7.21” storm inundation event to validate the usability and suitability of the proposed methods. In comparing the simulation results with in-situ observations, the proposed method is accurate and effective, with significantly lower drainage data requirements being obtained. The proposed methods will also be useful for urban drainage design and city inundation emergency preparations.
Open AccessA New Method for Urban Storm Flood Inundation Simulation with Fine CD-TIN Surface
Urban storm inundation, which frequently has dramatic impacts on city safety and social life, is an emergent and difficult issue. Due to the complexity of urban surfaces and the variety of spatial modeling elements, the lack of detailed hydrological data and accurate urban surface models compromise the study and implementation of urban storm inundation simulations. This paper introduces a Constrained Delaunay Triangular Irregular Network (CD-TIN) to model fine urban surfaces (based on detailed ground sampling data) and subsequently employs a depression division method that refers to Fine Constrained Features (FCFs) to construct computational urban water depressions. Storm-runoff yield is placed through mass conservation to calculate the volume of rainfall, runoff and drainage. The water confluences between neighboring depressions are provided when the water level exceeds the outlet of a certain depression. Numerical solutions achieved through a dichotomy are introduced to obtain the water level. Therefore, the continuous inundation process can be divided into different time intervals to obtain a series of inundation scenarios. The main campus of Beijing Normal University (BNU) was used as a case study to simulate the “7.21” storm inundation event to validate the usability and suitability of the proposed methods. In comparing the simulation results with in-situ observations, the proposed method is accurate and effective, with significantly lower drainage data requirements being obtained. The proposed methods will also be useful for urban drainage design and city inundation emergency preparations.