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Accuracy and computational efficiency of 2D urban surface flood modelling based on cellular automata

机译:基于元胞自动机的二维城市地表洪水建模的精度与计算效率。

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摘要

There is an emerging abundance freely available of high resolution (one meter or less) LIDAR data due to the advent of remote sensing, which enables wider applications of detailed flood risk modelling and analysis. Digital terrain surface data often comes in raster form, i.e., a square regular grid, and often requires conversion into a specific computational mesh for two-dimensional (2D) flood modelling that adopts triangular irregular meshes. 2D modelling of flood water movement through urban areas requires resolution of complex flow paths around buildings, which requires both high accuracy and computational efficiency. Water distribution and Wastewater systems in the UK contain over 700,000 km of water distribution and sewer pipes, which represents a large risk exposure from flooding caused by sewer surcharging or distribution pipe breaks. This makes it important for utilities to understand and predict where clean or dirty water flows will be directed when they leave the system. In order to establish risk assessment many thousands of simulations may be required calling for the most computational efficient models possible. Cellular Automata (CA) represents a method of running simulations based on a regular square grid, thus saving set-up time of configuring the terrain data into an irregular triangular mesh. It also offers a more uniform memory pattern for very fast modern, highly parallel hardware, such as general purpose graphical processing units (GPGPU). In this paper the performance of the CADDIES, a CA platform and associate flood modelling software caFloodPro, using a square regular grid and Von Neumann neighbourhood, is compared to industry standard software using triangular irregular meshes for similar resolutions. A minimum time step is used to control the computational complexity of the algorithm, which then creates a trade-off between the processing speeds of simulations and the accuracy resulting from the limitations used within the local rule to cope with relatively large time steps. This study shows that using CA based methods on regular square grids offers process speed increases in terms of 5-20 times over that of the industry standard software using irregular triangular meshes, while maintaining 98-99% flooding extent accuracy.
机译:由于遥感技术的出现,可以自由获取高分辨率(一米或更小)的高分辨率LIDAR数据,这使得详细洪水风险建模和分析的广泛应用成为可能。数字地形表面数据通常以栅格形式出现,即正方形规则网格,并且经常需要转换为使用三角形不规则网格的二维(2D)洪水建模的特定计算网格。洪水在城市中流动的2D建模需要解析建筑物周围复杂的流动路径,这需要高精度和计算效率。英国的供水和废水处理系统包含超过700,000公里的供水和下水管道,这代表着下水道收费或下水管道中断造成的洪水风险。因此,对于公用事业公司来说,重要的是要了解和预测清洁或污水流离开系统时将流向何处。为了建立风险评估,可能需要成千上万的模拟,要求尽可能多的计算有效模型。元胞自动机(Cellular Automata,CA)表示一种基于规则正方形网格运行模拟的方法,从而节省了将地形数据配置为不规则三角形网格的设置时间。它还为非常快的现代,高度并行的硬件(例如通用图形处理单元(GPGPU))提供了更加统一的存储模式。在本文中,将使用正方形规则网格和Von Neumann社区的CADDIES,CA平台和相关洪水建模软件caFloodPro的性能与使用三角形不规则网格的行业标准软件进行比较,以得到相似的分辨率。最小时间步长用于控制算法的计算复杂度,然后在模拟的处理速度与精度之间进行权衡,该精度是局部规则在处理相对较大的时间步长时所受限制的结果。这项研究表明,在规则正方形网格上使用基于CA的方法,与使用不规则三角形网格的行业标准软件相比,处理速度提高了5-20倍,同时保持了98-99%的泛洪范围精度。

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