A Quantitative Flood-Related Building Damage Evaluation Method Using Airborne LiDAR Data and 2-D Hydraulic Model

Shen, Di; Qian, Tianlu; Chen, Wenlong; Chi, Yao; Wang, Jiechen

doi:10.3390/w11050987

Cited by 10 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this sense, as indicated by Vaze et al [32], the use of LiDAR data has significantly improved the terrain representation in the DTMs. However, the urban areas (the majority in the location of cultural heritage sites) remain the most problematic and have the highest uncertainty, both in the correct representation of urban areas (e.g., References [33][34][35]) and in the results of the hydrodynamic models used to estimate flood hazard (e.g., References [31,36]).…”

Section: Discussionmentioning

confidence: 99%

A Framework Proposal for Regional-Scale Flood-Risk Assessment of Cultural Heritage Sites and Application to the Castile and León Region (Central Spain)

Garrote

Díez‐Herrero

Escudero³

et al. 2020

Water

View full text Add to dashboard Cite

Floods, at present, may constitute the natural phenomenon with the greatest impact on the deterioration of cultural heritage, which is the reason why the study of flood risk becomes essential in any attempt to manage cultural heritage (archaeological sites, historic buildings, artworks, etc.) This management of cultural heritage is complicated when it is distributed over a wide territory. This is precisely the situation in the region of Castile and León (Spain), in which 2155 cultural heritage elements are registered in the Catalog of Cultural Heritage Sites of Castile and León, and these are distributed along the 94,226 km2 of this region. Given this scenario, the present study proposes a methodological framework of flood risk analysis for these cultural heritage sites and elements. This assessment is based on two main processing tools to be developed in addition: on the one hand, the creation of a GIS database in which to establish the spatial relationship between the cultural heritage elements and the flow-prone areas for different flood return periods and, on the other hand, the creation of a risk matrix in which different variables are regarded as associated both to flood hazard (return period, flow depth, and river flooding typology) and to flood vulnerability (construction typology, and construction structural relationship with the hydraulic environment). The combination of both tools has allowed us to establish each cultural heritage flood risk level, making its categorization of risk possible. Of all the cultural heritage sites considered, 18 of them are categorized under an Extreme flood risk level; and another 24 show a High potential flood risk level. Therefore, these are about 25% to 30% of all cultural heritage sites in Castile and León. This flood risk categorization, with a scientific basis of the cultural heritage sites at risk, makes it possible to define territories of high flood risk clustering; where local scale analyses for mitigation measures against flood risk are necessary.

show abstract

Section: Discussionmentioning

confidence: 99%

A Framework Proposal for Regional-Scale Flood-Risk Assessment of Cultural Heritage Sites and Application to the Castile and León Region (Central Spain)

Garrote

Díez‐Herrero

Escudero³

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…Of these, 3379 buildings were misclassified and manually eliminated, leaving 30,551 buildings in the study area. The accuracy rate was 90.04% [24]. Buildings are vulnerable to flood hazards and their risk levels are related to the building type, replacement cost and submerged depth.…”

Section: (8) Buildingsmentioning

confidence: 97%

“…Macro-scale, high-resolution DEM data products have become common, which has popularized the application of such data to flood simulation research [19][20][21]. This has provided new opportunities for FHR research, such as micro-scale FHR mapping [22], flood damage assessment for individual buildings [23,24] and micro-scale FHR analysis [25]. The computational complexity of the models used in these studies increases dramatically when their computational grids become more refined.…”

Section: Introductionmentioning

confidence: 99%

Micro-scale Flood Hazard Assessment Based on Catastrophe Theory and an Integrated 2-D Hydraulic Model: A Case Study of Gongshuangcha Detention Basin in Dongting Lake Area, China

Shen

Qian

Xia

et al. 2020

IJGI

Self Cite

View full text Add to dashboard Cite

Assessments of urban flood hazards are crucial for planning and early warning flood system design. Moreover, hazard risk assessment is useful for emergency planning and insurance. There are two common methods for conducting flood hazard risk assessments (FHRA): those based on physical models and those based on parameters. Although physical models are able to simulate flood propagation processes accurately, they also have obvious shortcomings. Parameter-based FHRAs are more comprehensive because they emphasize the analysis of hazard factors. However, this approach also has various flaws, including its qualitative, macro-scale and high subjective nature. In this study, the strengths of both methods were combined to develop a new micro-scale FHRA. Taking the FHRA of the flood storage and detention area of Dongting Lake as an example, this study used high-precision digital elevation model (DEM) data generated from an airborne light detection and ranging (LiDAR) point cloud to construct a two-dimensional (2-D) flood propagation model. Micro-scale FHRAs were then performed using eight selected FHR indicators based on catastrophe theory. By automatically calculating the FHR value of each assessment unit based on hierarchical recursion, the catastrophe theory and catastrophe progression method effectively avoided uncertainty in weight assignment, which is an issue commonly faced by parameter-based methods. The FHRA results obtained under 144 different sequences of assessment indicators also show that the proposed method has a low sensitivity to the ranking of FHR indicators, as well as a high fault tolerance for different assessment results arising from subjective rankings by humans.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

A Quantitative Flood-Related Building Damage Evaluation Method Using Airborne LiDAR Data and 2-D Hydraulic Model

Cited by 10 publications

References 30 publications

A Framework Proposal for Regional-Scale Flood-Risk Assessment of Cultural Heritage Sites and Application to the Castile and León Region (Central Spain)

A Framework Proposal for Regional-Scale Flood-Risk Assessment of Cultural Heritage Sites and Application to the Castile and León Region (Central Spain)

Micro-scale Flood Hazard Assessment Based on Catastrophe Theory and an Integrated 2-D Hydraulic Model: A Case Study of Gongshuangcha Detention Basin in Dongting Lake Area, China

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

Contact Info

Product

Resources

About