Comparison of six approaches in DTM reduction for flood risk determination

Krzysztof Bakuła

Abstract

Software for creating flood risk maps and simulation of flood water is based on Digital Terrain Model (DTM). LIDAR is the most effective data source for DTM creation. The essential problem for such data is high redundancy for complex calculations in algorithms used in programs for flood model description. However, it is possible to provide suitable DTM for flood modeling by its generalization, which could still ensure sufficient accuracy for hydrodynamic — numerical calculations. In this paper six generalization algorithms were tested to obtain DTM with small number of points and with accuracy comparable to the original model created from LIDAR data. The main criteria for this comparison was the relation between accuracy and reduction coefficient of final result. Methods used in this research based on different DTM structures. GRID, hybrid and hierarchical structure were compared in various creating approaches to obtain the most reduced and the most accurate terrain model of two case areas. As the result of experiment the best methods for data reduction were chosen. Over 90% reduction rate and less than 20 cm root mean standard error were achieved for different types of terrain with respect to input DTM. It was noted that hybrid models can be more efficient than a typical uniform GRID. Such reduced models were used in practice to determinate fl ood risk by creating maps of flooded area for selected water levels. Diff erence between results from unreduced and reduced DTMs was very slight what proves that well-generalized models of terrain can be effectively used in that application.
Author Krzysztof Bakuła (FGC / DPTSIS)
Krzysztof Bakuła,,
- Department of Photogrammetry, Teledetection and Spatial Information Systems
Journal seriesChallenges of Modern Technology, ISSN 2082-2863, (B 2 pkt)
Issue year2011
Vol2
No4
Pages31-36
Internal identifier3/2011
Languageen angielski
File
2011_Bakula_Comparison_of_six_3.pdf 806.84 KB
Score (nominal)2
Citation count*4 (2020-02-12)
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