Identification of Urban Rainstorm Waterlogging Based on Multi-source Information Fusion：A Case Study in Futian District, Shenzhen

Zhang, Zongjia; Meng, Fanyu; Zeng, Yiping; Liu, Junguo; Yuan, Diping; Fong, Simon; Yang, Lili

doi:10.1051/e3sconf/202125901004

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…where σ j is the discrete degree of the proportion of all POI types in the j-th grid unit. The smaller the value, the smaller the gap in the contribution value of the six types of POIs, the more balanced the degree of development of various functions, the greater the degree of functional mixing of the grid unit, and the greater the overall development degree [57].…”

Section: Mixing Degree Of the Poi Functionmentioning

confidence: 99%

Urban Spatial Image Acquisition and Examination Based on Geographic Big Data

Zhou,

Li,

et al. 2024

Land

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This study proposes a two-dimensional analytical framework based on urban spatial form and spatial service perspectives, utilizing data on buildings and points of interest (POIs). It integrates fishnet analysis, kernel density analysis, the categorization of POI functionalities, and mixture calculations to enhance our understanding of urban spatial form and function. Taking the main urban area of Zhengzhou as an example, this study identifies image elements that can describe urban spatial characteristics through the results of two-dimensional analysis and enriches the city image in the form of a portrait. The experimental findings demonstrate that the elements of the annular layer, functional landmarks, ring line boundaries, and special districts can fully convey the spatial picture of Zhengzhou City. The performance of the four types of image elements has a high degree of matching with the content of the urban spatial planning of Zhengzhou City, which can effectively identify the urban multi-center structure and development pattern. This paper explores and tests the development status of the city from a new perspective, which can provide an effective reference for the future planning and sustainable development of the city.

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Section: Mixing Degree Of the Poi Functionmentioning

confidence: 99%

Urban Spatial Image Acquisition and Examination Based on Geographic Big Data

Zhou,

Li,

et al. 2024

Land

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“…Hydrodynamic equations can provide accurate calculations for the waterlogging runoff. In this paper, we use our algorithm for calculating the depth of a waterlogged runoff with the nearest road as the evolution path to achieve the depth and flow rate [35].…”

Section: Waterlogging Depth Calculationmentioning

confidence: 99%

Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification

Zhang

Zeng

Huang

et al. 2023

IJERPH

Self Cite

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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.

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“…However, coupled research models reduce flood simulation time at the expense of accuracy (LISFLOOD) or use cellular automate (CA) approaches instead of solving shallow water equations (CADDIES). Commercial integrated models, especially MIKE Flood, have also been widely employed in the GBA, including Guangzhou (Liu, Cai, Wang, Lan, & Wu, 2020), Shenzhen (Zhang et al, 2021), and Zhuhai (Yin et al, 2020). MIKE Flood was chosen as the simulation model in this study due to its accuracy of simulation and the convenience of validation parameters in the surrounding area.…”

mentioning

confidence: 99%

Scenario‐based hydrodynamic simulation of adaptive strategies for urban design to improve flood resilience: A case study of the Mingzhu Bay Region, Guangzhou, Greater Bay Area

Sun

2021

River Research & Apps

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The Pearl River Delta is a complex ecological‐artificial system. The low‐elevation landform and land subsidence make the Guangdong‐Hong Kong‐Macao Greater Bay Area (GBA) more vulnerable to heavy rainstorms with sea‐level rise in the future. Therefore, this study discussed how to optimize the delta‐related built environment based on locality via cooperation between urban designers and hydrology researchers so that urban development in the GBA can adapt to water disasters. First, this study proposed three adaptive spatial strategies for urban design to improve flood resilience: (a) adding more sunken space and rearranging the locations of manholes; (b) adding more retention space to the waterway system; and (c) improving foundation elevation for suitable development intensity. With the prediction of sea‐level rise and land subsidence, four different scenarios were defined. Second, a coupled 1D/2D hydrodynamic model was built for scenario‐based urban inundation simulations. Third, a Python workflow was developed to reveal the water volume exchange, and an ArcMap workflow to realize the impact of inundation. The simulation results show that (a) more sunken space can lower the peak runoff discharges and decrease the runoff coefficient; (b) more retention space can extend the capacity of the urban drainage system and adapt to the rise of sea level; and (c) improving foundation elevation and more sunken green space can direct the surface water to submerge urban land in reasonable order, which can protect the significant infrastructures and development lands. This paper suggests that these adaptive spatial strategies can serve as solutions for resilience development.

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Identification of Urban Rainstorm Waterlogging Based on Multi-source Information Fusion：A Case Study in Futian District, Shenzhen

Cited by 3 publications

References 14 publications

Urban Spatial Image Acquisition and Examination Based on Geographic Big Data

Urban Spatial Image Acquisition and Examination Based on Geographic Big Data

Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification

Scenario‐based hydrodynamic simulation of adaptive strategies for urban design to improve flood resilience: A case study of the Mingzhu Bay Region, Guangzhou, Greater Bay Area

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