scholarly journals A methodology for processing raw LiDAR data to support urban flood modelling framework

2011 ◽  
Vol 14 (1) ◽  
pp. 75-92 ◽  
Author(s):  
A. F. Abdullah ◽  
Z. Vojinovic ◽  
R. K. Price ◽  
N. A. A. Aziz
Keyword(s):  
Lidar Data ◽  
Urban Flood ◽  
Flood Modelling ◽  
Filtering Algorithm ◽  
Modelling Framework ◽  
Significant Difference ◽  
Morphological Filtering ◽  
Recorded Data ◽  
The Difference ◽  
Urban Flood Modelling

An assessment has been carried out to study the performance of seven different LiDAR filtering algorithms and to evaluate their suitability for urban flood modelling applications. It was found that none of these algorithms can be regarded as fully suitable to support such work in its present form. The paper presents the augmentation of an existing Progressive Morphological filtering algorithm for processing raw LiDAR data to support a 1D/2D urban flood modelling framework. The existing progressive morphological filtering algorithm was modified to incorporate buildings with basement, passage buildings and solid buildings and its value was demonstrated on a case study from Kuala Lumpur, Malaysia. The model results were analysed and compared against recorded data in terms of flood depths, flood extents and flood velocities. The difference in flood depths of approximately 40% was observed between a model that uses a DTM modified by the progressive morphological filtering algorithm and the predictions of other models. The overall results suggest that incorporation of building basements within the DTM can lead to a significant difference in the model results with a tendency towards overestimation for those models which do not incorporate such a feature.

scholarly journals Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data

2013 ◽  
Vol 10 (5) ◽  
pp. 5903-5942 ◽  
Author(s):  
H. Ozdemir ◽  
C. C. Sampson ◽  
G. A. M. de Almeida ◽  
P. D. Bates
Keyword(s):  
Surface Water ◽  
Water Depth ◽  
Arrival Time ◽  
Surface Friction ◽  
Lidar Data ◽  
Urban Flood ◽  
Terrestrial Lidar ◽  
Flood Modelling ◽  
Depth Extent ◽  
Urban Flood Modelling

Abstract. This paper evaluates the results of benchmark testing a new inertial formulation of the de St. Venant equations, implemented within the LISFLOOD-FP hydraulic model, using different high resolution terrestrial LiDAR data (10 cm, 50 cm and 1 m) and roughness conditions (distributed and composite) in an urban area. To examine these effects, the model is applied to a hypothetical flooding scenario in Alcester, UK, which experienced surface water flooding during summer 2007. The sensitivities of simulated water depth, extent, arrival time and velocity to grid resolutions and different roughness conditions are analysed. The results indicate that increasing the terrain resolution from 1 m to 10 cm significantly affects modelled water depth, extent, arrival time and velocity. This is because hydraulically relevant small scale topography that is accurately captured by the terrestrial LIDAR system, such as road cambers and street kerbs, is better represented on the higher resolution DEM. It is shown that altering surface friction values within a wide range has only a limited effect and is not sufficient to recover the results of the 10 cm simulation at 1 m resolution. Alternating between a uniform composite surface friction value (n = 0.013) or a variable distributed value based on land use has a greater effect on flow velocities and arrival times than on water depths and inundation extent. We conclude that the use of extra detail inherent in terrestrial laser scanning data compared to airborne sensors will be advantageous for urban flood modelling related to surface water, risk analysis and planning for Sustainable Urban Drainage Systems (SUDS) to attenuate flow.

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

2018 ◽  
Vol 103 ◽  
pp. 131-145 ◽  
Author(s):  
Seong Jin Noh ◽  
Jun-Hak Lee ◽  
Seungsoo Lee ◽  
Kenji Kawaike ◽  
Dong-Jun Seo
Keyword(s):  
Lidar Data ◽  
Urban Flood ◽  
Flood Modelling ◽  
Hybrid Parallelization ◽  
Urban Flood Modelling

Urban flood modelling combining top-view LiDAR data with ground-view SfM observations

2015 ◽  
Vol 75 ◽  
pp. 105-117 ◽  
Author(s):  
Vorawit Meesuk ◽  
Zoran Vojinovic ◽  
Arthur E. Mynett ◽  
Ahmad F. Abdullah
Keyword(s):  
Lidar Data ◽  
Urban Flood ◽  
Flood Modelling ◽  
Top View ◽  
Urban Flood Modelling

scholarly journals Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data

2013 ◽  
Vol 17 (10) ◽  
pp. 4015-4030 ◽  
Author(s):  
H. Ozdemir ◽  
C. C. Sampson ◽  
G. A. M. de Almeida ◽  
P. D. Bates
Keyword(s):  
Surface Water ◽  
Water Depth ◽  
Arrival Time ◽  
Surface Friction ◽  
Lidar Data ◽  
Urban Flood ◽  
Terrestrial Lidar ◽  
Flood Modelling ◽  
Depth Extent ◽  
Urban Flood Modelling

Abstract. This paper evaluates the results of benchmark testing a new inertial formulation of the St. Venant equations, implemented within the LISFLOOD-FP hydraulic model, using different high resolution terrestrial LiDAR data (10 cm, 50 cm and 1 m) and roughness conditions (distributed and composite) in an urban area. To examine these effects, the model is applied to a hypothetical flooding scenario in Alcester, UK, which experienced surface water flooding during summer 2007. The sensitivities of simulated water depth, extent, arrival time and velocity to grid resolutions and different roughness conditions are analysed. The results indicate that increasing the terrain resolution from 1 m to 10 cm significantly affects modelled water depth, extent, arrival time and velocity. This is because hydraulically relevant small scale topography that is accurately captured by the terrestrial LIDAR system, such as road cambers and street kerbs, is better represented on the higher resolution DEM. It is shown that altering surface friction values within a wide range has only a limited effect and is not sufficient to recover the results of the 10 cm simulation at 1 m resolution. Alternating between a uniform composite surface friction value (n = 0.013) or a variable distributed value based on land use has a greater effect on flow velocities and arrival times than on water depths and inundation extent. We conclude that the use of extra detail inherent in terrestrial laser scanning data compared to airborne sensors will be advantageous for urban flood modelling related to surface water, risk analysis and planning for Sustainable Urban Drainage Systems (SUDS) to attenuate flow.

scholarly journals A Data-Driven Hybrid Urban Flood Modelling Approach

10.29007/fbh3 ◽  
2018 ◽  
Author(s):  
Xiaohan Li ◽  
Patrick Willems
Keyword(s):  
Data Availability ◽  
Data Driven ◽  
Property Management ◽  
Flood Modeling ◽  
Urban Flood ◽  
Flood Modelling ◽  
Data Sparseness ◽  
Input Quality ◽  
Fully Adaptive ◽  
Urban Flood Modelling

Urban flood pre-warning decisions made upon urban flood modeling is crucial for human and property management in urban area. However, urbanization, changing environmental conditions and climate change are challenging urban sewer models for their adaptability. While hydraulic models are capable of making accurate flood predictions, they are less flexible and more computationally expensive compared with conceptual models, which are simpler and more efficient. In the era of exploding data availability and computing techniques, data-driven models are gaining popularity in urban flood modelling, but meanwhile suffer from data sparseness. To overcome this issue, a hybrid urban flood modeling approach is proposed in this study. It incorporates a conceptual model to account for the dominant sewer hydrological processes and a logistic regression model able to predict the probabilities of flooding on a sub-urban scale. This approach is demonstrated for a highly urbanized area in Antwerp, Belgium. After comparison with a 1D/0D hydrodynamic model, its ability is shown with promising results to make probabilistic flood predictions, regardless of rainfall types or seasonal variation. In addition, the model has higher tolerance on data input quality and is fully adaptive for real time applications.

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