scholarly journals Comparison between Topographic and Bathymetric LiDAR Terrain Models in Flood Inundation Estimations

2022 ◽  
Vol 14 (1) ◽  
pp. 227
Author(s):  
Mahmoud Omer Mahmoud Awadallah ◽  
Ana Juárez ◽  
Knut Alfredsen

Remotely sensed LiDAR data has allowed for more accurate flood map generation through hydraulic simulations. Topographic and bathymetric LiDARs are the two types of LiDAR used, of which the former cannot penetrate water bodies while the latter can. Usually, the topographic LiDAR is more available than bathymetric LiDAR, and it is, therefore, a very interesting data source for flood mapping. In this study, we made comparisons between flood inundation maps from several flood scenarios generated by the HEC-RAS 2D model for 11 sites in Norway using both bathymetric and topographic terrain models. The main objective is to investigate the accuracy of the flood inundations generated from the plain topographic LiDAR, the links of the inaccuracies with geomorphic features, and the potential of using corrections for missing underwater geometry in the topographic LiDAR data to improve accuracy. The results show that the difference in inundation between topographic and bathymetric LiDAR models decreases with increasing the flood size, and this trend was found to be correlated with the amount of protection embankments in the reach. In reaches where considerable embankments are constructed, the difference between the inundations increases until the embankments are overtopped and then returns to the general trend. In addition, the magnitude of the inundation error was found to correlate positively with the sinuosity and embankment coverage and negatively with the angle of the bank. Corrections were conducted by modifying the flood discharge based on the flight discharge of the topographic LiDAR or by correcting the topographic LiDAR terrain based on the volume of the flight discharge, where the latter method generally gave better improvements.

2021 ◽  
Author(s):  
Robert E Criss ◽  
David L. Nelson

Abstract New methods allow the direct computation of flood inundation maps from lidar data, independently of discharge estimates, hydraulic analysis, or defined cross sections. One method projects the interpolated profile of measured flood levels onto surrounding topography, creating a smooth inundation surface that is entirely based on data and geometrical relationships. A second method computes inundation maps for any simple function that relates the water surface to the elevation of the channel bottom, exploiting their known, sub-parallel character. A final method theoretically combines the elevation of the channel bottom and the upstream catchment area for points along the thalweg, all defined by lidar data. The conceptual simplicity and realism of these maps facilitate data-based planning.


2002 ◽  
Author(s):  
David L. Kresch ◽  
Mark C. Mastin ◽  
T.D. Olsen

2002 ◽  
Author(s):  
David L. Kresch ◽  
Mark C. Mastin ◽  
T.D. Olsen

2021 ◽  
pp. 1-26
Author(s):  
Bikash Ranjan Parida ◽  
Gaurav Tripathi ◽  
Arvind Chandra Pandey ◽  
Amit Kumar

2021 ◽  
pp. 1-11
Author(s):  
Yanan Huang ◽  
Yuji Miao ◽  
Zhenjing Da

The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated.


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