An urban DEM reconstruction method based on multisource data fusion for urban pluvial flooding simulation

Huang, Haocheng; Liao, Weihong; Lei, Xiaohui; Wang, Chao; Cai, Zhipeng; Wang, Hao

doi:10.1016/j.jhydrol.2022.128825

Cited by 26 publications

(13 citation statements)

References 50 publications

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“…Digital Elevation Model (DEM) data as shown in Fig. 3 plays a crucial role in assessing ood susceptibility in any urban area (Huang et al 2023) by providing detailed information about the topography of an area, some of its uses are:…”

Section: Data Collectionmentioning

confidence: 99%

Flood Susceptibility Mapping to Identify the Vulnerable Areas in the Adayar River Basin at Chennai, Tamil Nadu

ASAITHAMBI,

Poddar,

Varatharajan

et al. 2024

Preprint

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The Adayar River Basin in Chennai, Tamil Nadu, is plagued by recurring inundation events, posing substantial hazards to human settlements and critical infrastructure. In response, this research endeavors to develop a flood susceptibility map to pinpoint regions within the basin prone to flooding. Leveraging Geographic Information Systems (GIS) and employing the Analytical Hierarchy Process (AHP) methodology via GIS software, an array of spatial and non-spatial variables influencing flood susceptibility were meticulously examined and weighted. By integrating diverse hydrological, geological, and meteorological parameters and applying AHP's pairwise comparison, a holistic understanding of flood susceptibility was attained. The GIS approach enables visualizing spatial patterns and identifying high-risk flood areas. In this paper, the flood susceptibility map has been characterized into five different classes which include Very High region, High region, Moderate region, Low region, and Very Low region, based on this characterization a total of 40 vulnerable areas have been identified with 10 very high susceptible areas followed by 16 highly susceptible areas and 14 moderately susceptible areas.

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Section: Data Collectionmentioning

confidence: 99%

Flood Susceptibility Mapping to Identify the Vulnerable Areas in the Adayar River Basin at Chennai, Tamil Nadu

ASAITHAMBI,

Poddar,

Varatharajan

et al. 2024

Preprint

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“…This study selected the Liwan District of Guangzhou as the case area and used the SWMM (Storm Water Management Model) and ITF [28] (Integrated Terrestrial Fluxes Model) to construct a one-dimensional and two-dimensional coupled torrential rain and waterlogging model. This study evaluated the accessibility of urban emergency rescue services under torrential rain and waterlogging conditions using the Two-Step Floating Catchment Area (2SFCA) method and simulated the deployment of mobile pump trucks and their drainage effects in practice through the two-dimensional model.…”

Section: Introductionmentioning

confidence: 99%

Assessment and Improvement of Emergency Rescue Service Accessibility under Urban Waterlogging Disasters

Shi,

Wang,

Zhou

et al. 2024

Water

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With the acceleration of urbanization and the impact of climate change, the frequent occurrence of urban waterlogging not only leads to road closures and traffic congestion but also severely affects the timeliness of urban emergency rescue. To accurately assess and enhance the response capability of urban emergency rescue under storm-induced waterlogging scenarios, a hydrodynamic model of urban waterlogging was developed to simulate waterlogging conditions under various design rainfall scenarios. By identifying road waterlogging risks and blocked roads, as well as combining the Two-Step Floating Catchment Area (2SFCA) method, the accessibility of emergency rescue services for points of interest (POIs) with different vulnerabilities was evaluated. The Liwan District of Guangzhou City was selected as a case study for accessibility impact assessment and improvement simulation. The results indicate that with the increase in the return period of rainfall, both the area and depth of waterlogged regions increased and the number of roads affected by waterlogging rose, leading to an increase in the length of blocked roads from 11 km to 49 km, an increase of over 300%. Additionally, the number of POIs inaccessible to emergency rescue increased, while the number of accessible POIs decreased, resulting in a significant downward trend in overall accessibility. By deploying mobile pumping vehicles, the depth and area of waterlogging under different rainfall return periods were reduced by over 10%, the number of blocked roads decreased by more than 10%, and the number of accessible POIs increased by more than 12%. The findings highlight that storm-induced waterlogging not only hinders traffic flow but also reduces the response capability of emergency rescue services. Through the strategic deployment of mobile pumping vehicles, the accessibility of urban emergency rescue services under waterlogging conditions can be effectively improved, mitigating the impact of waterlogging on urban functions and public safety.

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“…Flood simulation is critically dependent on modeling, and the accuracy and resolution of the digital elevation model (DEM) have a signi cant in uence on the simulation results such as inundated area, ow velocity, inundated depth, ow patterns, and so on (Huang et al 2023;Xu et al 2014; Zhao and Wang 2022). However, the DEM for ood simulation usually relies on static data from a geographic information system (GIS), which may be outdated and cannot re ect the current environment or the changes in runoff characteristics that are critical to the ood routing analysis.…”

Section: Introductionmentioning

confidence: 99%

“…However, the DEM for ood simulation usually relies on static data from a geographic information system (GIS), which may be outdated and cannot re ect the current environment or the changes in runoff characteristics that are critical to the ood routing analysis. In addition, more accuracy requirements of DEM are needed for ood risk mapping in low-lying or at areas (Huang et al 2023). Referring to the European handbook for ood modeling (European Commission 2007), the resolution requirements are horizontally and 0.5 m vertically.…”

Section: Introductionmentioning

confidence: 99%

A Rapid Assessment Method for Flood Risk Mapping Integrating Aerial Point Clouds and Deep Learning

Fang,

Wu,

Jiang

et al. 2024

Water Resour Manage

Self Cite

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In recent years, oods have brought renewed attention and requirement for real-time and city-scaled ood forecasting, due to climate change and urbanization. Flood risk mapping through traditional physicsbased modeling methods is often unrealistic for rapid emergency response requirements, because of long model runtime, hydrological information lacking, and terrain change caused by human activity. In this study, an automated simulation framework is proposed by integrating aerial point clouds and deep learning technique that is capable of superior modeling e ciency and analysis accuracy for ood risk mapping. The framework includes four application modules, i.e., data acquisition and preprocessing, point clouds segmentation, digital elevation model (DEM) reconstruction, and hydrodynamics simulation.To more clearly demonstrate the advantages of the proposed automated simulation framework, a case study is conducted in a local area of the South-to-North Water Transfer Project in China. In addition, the e ciency and accuracy of the suggested point cloud segmentation network for large-scale 3D point clouds in basin scenes are discussed in detail by comparison with PointNet and PointNet + + networks.

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An urban DEM reconstruction method based on multisource data fusion for urban pluvial flooding simulation

Cited by 26 publications

References 50 publications

Flood Susceptibility Mapping to Identify the Vulnerable Areas in the Adayar River Basin at Chennai, Tamil Nadu

Flood Susceptibility Mapping to Identify the Vulnerable Areas in the Adayar River Basin at Chennai, Tamil Nadu

Assessment and Improvement of Emergency Rescue Service Accessibility under Urban Waterlogging Disasters

A Rapid Assessment Method for Flood Risk Mapping Integrating Aerial Point Clouds and Deep Learning

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