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

Zongjia Zhang; Yiping Zeng; Zhejun Huang; Junguo Liu; Lili Yang

doi:10.3390/ijerph20032528

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

Int J Environ Res Public Health. 2023 Jan 31;20(3):2528. doi: 10.3390/ijerph20032528.

Authors

Zongjia Zhang^{1

2}, Yiping Zeng², Zhejun Huang², Junguo Liu^{3

4}, Lili Yang²

Affiliations

¹ School of Environment, Harbin Institute of Technology, Harbin 150001, China.
² Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China.
³ School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
⁴ Henan Provincial Key Laboratory of Hydrosphere and Watershed Water Security, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.

Abstract

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.

Keywords: GIS; disaster risk assessment; environmental risk; flood prediction; hydrodynamics; multi-source data; risk identification; urban waterlogging.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biological Products*
China
Cities
Disasters*
Hydrodynamics
Rain
Water Movements

Substances

Biological Products

Grants and funding

This research was funded by the National Key R&D Program of China (2018YFC0807000), Natural Science Foundation of China (71771113), National Key R&D Program of China (2019YFC0810705), Shenzhen Scientific Research Funding (Grant No. K22627501), and Shenzhen Science and Technology Plan platform and carrier special (grant no. ZDSYS20210623092007023). It was also partly supported by the Shenzhen Science and Technology Program (KCXFZ20201221173601003) and the Henan Provincial Key Laboratory of Hydrosphere and Watershed Water Security.