Hyper-resolution 1D-2D urban flood modelling using LiDAR data and hybrid parallelization

Noh, Seong Jin; Lee, Jun Hak; Lee, Seungsoo; Kawaike, Kenji; Seo, Dong‐Jun

doi:10.1016/j.envsoft.2018.02.008

Cited by 69 publications

(34 citation statements)

References 61 publications

(74 reference statements)

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“…Although urban flooding is affected by various natural and man-made controls [9], mechanisms of inundation can be largely divided into two types: fluvial flooding (i.e., inundation of river flow) and pluvial flooding (i.e., flash flooding on the surface). The flow spills out of rivers toward floodplains and leads to fluvial flooding, while pluvial flooding is induced by extreme rainfall.…”

Section: Introductionmentioning

confidence: 99%

“…The last two decades have seen increasingly rapid advances in urban flood modeling especially in relation with high-resolution approaches [9][10][11][12][13][14]. Liang et al [15] successfully applied a two-dimensional (2D) quadtree model on the River Thames in case the flood defenses were breached by the river at Thamesmead.…”

Section: Introductionmentioning

confidence: 99%

“…For assessment of the aforementioned factors, high-resolution inundation modeling at a city-wide scale is necessary, which cannot be easily addressed with typical urban models. H12, the hybrid code for 1D/2D urban flood modeling used in this study, can simulate pluvial urban flooding at a high resolution [17,19] with the capacity for large-scale modeling through hybrid parallelization [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Retrospective Dynamic Inundation Mapping of Hurricane Harvey Flooding in the Houston Metropolitan Area Using High-Resolution Modeling and High-Performance Computing

Noh

Lee

et al. 2019

Water

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Hurricane Harvey was one of the most extreme weather events to occur in Texas, USA; there was a huge amount of urban flooding in the city of Houston and the adjoining coastal areas. In this study, we reanalyze the spatiotemporal evolution of inundation during Hurricane Harvey using high-resolution two-dimensional urban flood modeling. This study’s domain includes the bayou basins in and around the Houston metropolitan area. The flood model uses the dynamic wave method and terrain data of 10-m resolution. It is forced by radar-based quantitative precipitation estimates. To evaluate the simulated inundation, on-site photos and water level observations were used. The inundation extent and severity are estimated by combining the retrieved water depths, images collected from the impacted area, and high-resolution terrain data. The simulated maximum inundation extent, which is frequently found outside of the designated flood zones, points out the importance of capturing multi-scale hydrodynamics in the built environment under extreme rainfall for effective flood risk and emergency management.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retrospective Dynamic Inundation Mapping of Hurricane Harvey Flooding in the Houston Metropolitan Area Using High-Resolution Modeling and High-Performance Computing

Noh

Lee

et al. 2019

Water

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“…Light detection and ranging (LiDAR) has become a widely used method because it can rapidly collect three-dimensional information over large areas with high horizontal resolution and high vertical accuracy. LiDAR has become one of the most used data sources in flood modeling [9,10], however, there are some limitations, such as high instrument and survey costs, especially for small study areas [11]; large data size; cumbersome classification of data; long computational time; and limited availability.…”

Section: Data Sources To Obtain Digital Elevation Models (Dems) For Fmentioning

confidence: 99%

Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

et al. 2019

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Digital elevation model (DEM) has been frequently used for the reduction and management of flood risk. Various classification methods have been developed to extract DEM from point clouds. However, the accuracy and computational efficiency need to be improved. The objectives of this study were as follows: (1) to determine the suitability of a new method to produce DEM from unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) data, using a raw point cloud classification and ground point filtering based on deep learning and neural networks (NN); (2) to test the convenience of rebalancing datasets for point cloud classification; (3) to evaluate the effect of the land cover class on the algorithm performance and the elevation accuracy; and (4) to assess the usability of the LiDAR and UAV structure from motion (SfM) DEM in flood risk mapping. In this paper, a new method of raw point cloud classification and ground point filtering based on deep learning using NN is proposed and tested on LiDAR and UAV data. The NN was trained on approximately 6 million points from which local and global geometric features and intensity data were extracted. Pixel-by-pixel accuracy assessment and visual inspection confirmed that filtering point clouds based on deep learning using NN is an appropriate technique for ground classification and producing DEM, as for the test and validation areas, both ground and non-ground classes achieved high recall (>0.70) and high precision values (>0.85), which showed that the two classes were well handled by the model. The type of method used for balancing the original dataset did not have a significant influence in the algorithm accuracy, and it was suggested not to use any of them unless the distribution of the generated and real data set will remain the same. Furthermore, the comparisons between true data and LiDAR and a UAV structure from motion (UAV SfM) point clouds were analyzed, as well as the derived DEM. The root mean square error (RMSE) and the mean average error (MAE) of the DEM were 0.25 m and 0.05 m, respectively, for LiDAR data, and 0.59 m and -0.28 m, respectively, for UAV data. For all land cover classes, the UAV DEM overestimated the elevation, whereas the LIDAR DEM underestimated it. The accuracy was not significantly different in the LiDAR DEM for the different vegetation classes, while for the UAV DEM, the RMSE increased with the height of the vegetation class. The comparison of the inundation areas derived from true LiDAR and UAV data for different water levels showed that in all cases, the largest differences were obtained for the lowest water level tested, while they performed best for very high water levels. Overall, the approach presented in this work produced DEM from LiDAR and UAV data with the required accuracy for flood mapping according to European Flood Directive standards. Although LiDAR is the recommended technology for point cloud acquisition, a suitable alternative is also UAV SfM in hilly areas.

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“…Meesuk et al [23] demonstrated the accuracy of LiDAR data for a numerical flood model. Noh et al [24] developed a hybrid parallel code for the 1D-2D-coupled urban flood modeling using DEM data by LiDAR with 1-m resolution. However, some complex flow features such as shock-type flow discontinuities, transcritical flows, as well as wetting and drying over irregular topography were induced, and therefore a robust numerical scheme was required to produce accurate and stable numerical solutions [25].…”

Section: Introductionmentioning

confidence: 99%

Effects of Bridge Piers on Flood Hazards: A Case Study on the Jialing River in China

Wen

Jing

Zuo

et al. 2019

Water

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Bridge piers on river channels can cause obstacles for flood flow by reducing the cross-sectional area and inducing local eddy currents and high flow velocities, which may destroy hydraulic structures. A two-dimensional numerical model was used to investigate the effects of bridge piers on river flood hazards in the Jialing River, China. For the modeling, Mike 21 FM was used, which is an unstructured mesh and finite volume model that solves the shallow water equations. The numerical model was validated with collected historical flood traces, and sensitivity analyses identified the effects of the Manning coefficient and the dependence on the grid size. The influence of backwater effects on the flow field was analyzed by comparing numerical results with and without piers. The results showed that the most significant impacts were caused by the Fengxian Bridge. The maximum water level rise was about 1 m and the maximum velocity near this bridge decreased by 22.77% for a 10-year flood. We found that the top elevations of planned levees near the bridges must be increased by 0.15-0.36 m. The influence of bridge piers on the flood velocity field is more complex. These findings will help flood hazard management in this river and provides a reference for similar projects.

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Hyper-resolution 1D-2D urban flood modelling using LiDAR data and hybrid parallelization

Cited by 69 publications

References 61 publications

Retrospective Dynamic Inundation Mapping of Hurricane Harvey Flooding in the Houston Metropolitan Area Using High-Resolution Modeling and High-Performance Computing

Retrospective Dynamic Inundation Mapping of Hurricane Harvey Flooding in the Houston Metropolitan Area Using High-Resolution Modeling and High-Performance Computing

Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

Effects of Bridge Piers on Flood Hazards: A Case Study on the Jialing River in China

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