Point Flood Query Based on Fast Binary Merge Tree

Ye Wu, Xuqiao Wu, Luo Chen
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Abstract

In recent years, flood-risk analysis has played an important role in the evaluation of submergence and flooding simulation. In this paper, we study a flood point query problem: Given a terrain T and total volume of rainfall in the area, determine if a given point is flooded. Existing methods build a contour tree by exacting all data points from T, and then get a merge tree, which is complicated with increasing resolution of terrains. Given the volume of rain, this paper proposes a flood analysis algorithm based on a fast binary merge tree generation. By eliminating the invalid saddle vertices in the data, the algorithm directly establishes the merger tree according to the corresponding contour hierarchy. Besides, the area and volume are also attached to enrich the merge tree. We describe a suite of experimental results showing the performance of our algorithm in practice and the running time of the preprocessing step is greatly reduced.
基于快速二叉合并树的点泛洪查询
近年来,洪水风险分析在淹没评价和洪水模拟中发挥了重要作用。本文研究了一个洪水点查询问题:给定地形T和该区域的总降雨量,确定给定点是否被洪水淹没。现有方法通过从T中提取所有数据点来构建轮廓树,然后得到合并树,随着地形分辨率的提高,该方法变得复杂。针对降雨的情况,提出了一种基于快速二叉合并树生成的洪水分析算法。该算法通过剔除数据中无效的鞍点,直接根据相应的轮廓层次建立合并树。此外,还附加了面积和体积,以丰富合并树。我们描述了一组实验结果,表明了我们的算法在实践中的性能,并且大大减少了预处理步骤的运行时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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