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

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.

Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

“…We define the wetting coefficient C w as Eq. 2 (Zhang et al, 2023), which is the ratio between the mean value of rainfall of this rainfall event and the rainfall interval δ, representing the wetting capacity of this rainfall on the land. Horton infiltration curves are commonly used in the field of hydrology to model the rate variation of fluid infiltration in different surfaces.…”

Identification of Waterlogging-Prone Areas in Nanning from the Perspective of Urban Expansion

“…Alternatively, the DEM topographic index was utilized to delineate waterlogging-prone areas in the Mohanadi Basin, India [61]. A multifaceted fusion of topographic, subsurface, transportation and meteorological elements was used to derive waterlogging-prone areas using hydrologic analysis [62]. The study attributes waterlogging to a combination of topography, slope, water accumulation and land use [61,63,64].…”

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

“…Data fusion techniques have become increasingly important in this regard, as they play a vital role in improving the reliability and interpretability of remotely sensed data [4]. These techniques enable the combination of diverse data types, formats, spatial and temporal scales, and other characteristics to create a unified and comprehensive view [5]. Data fusion is widely applied in various fields, including remote sensing [6], robotics, surveillance, and medical science.…”