I/O-Efficient Event Based Depression Flood Risk Article Swipe

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Lars Arge , Mathias Rav , Sarfraz Raza , Morten Revsbæk ·

YOU? · · 2017 · Open Access · · DOI: https://doi.org/10.1137/1.9781611974768.21

An important problem in terrain analysis is modeling how water flows across a terrain and creates floods by filling up depressions. The accuracy of such modeling depends critically on the precision of the terrain data, and available high-resolution terrain models of even fairly small geographic regions often exceed the size of a computer's main memory. In such cases movement of data between main memory and external memory (such as disk) is often the bottleneck in the computation. Thus it is important to develop I/O-efficient modeling algorithms, that is, algorithms that minimize the movement of blocks of data between main memory and disk.In this paper we develop practically I/O-efficient algorithms for the problem of computing the areas of a terrain that are flooded in a given flash flood event due to water collecting in depressions. Previous work only considered events where rain falls at a constant uniform rate on the entire terrain. In reality, local extreme flash floods can affect downstream areas that do not receive heavy rainfall directly, so it is important to model such non-uniform events. Our main algorithm uses 풪(Sort(N)+Scan(H·X)) I/Os, where N is the size of the terrain, Sort(N) and Scan(N) are the number of I/Os required to sort and read N elements in the standard two-level I/O-model, respectively, X is the number of sinks in the terrain and H the height of the so-called merge-tree, which is a hierarchical representation of the depressions of the terrain. Under practically realistic assumptions about the main memory size compared to X and H, we also develop 풪(Sort(N)) I/O-algorithms. One of these algorithms can handle an event in optimal 풪(Scan(N)) I/Os after using 풪(Sort(N)) I/Os on preprocessing the terrain. We have implemented our algorithms and show that they work very well in practice.

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Terrain sort Computer science Flash flood Flood myth Quadtree Merge (version control) Bottleneck Computation Algorithm Parallel computing Geography Database Cartography Archaeology Embedded system

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Type: article
Language: en
Landing Page: https://doi.org/10.1137/1.9781611974768.21
PDF: https://epubs.siam.org/doi/pdf/10.1137/1.9781611974768.21
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Cited By: 6
Related Works: 10
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DOI: https://doi.org/10.1137/1.9781611974768.21

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Title: I/O-Efficient Event Based Depression Flood Risk

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Language: en

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Publication year: 2017

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Publication date: 2017-01-01

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Authors: Lars Arge, Mathias Rav, Sarfraz Raza, Morten Revsbæk

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Landing page: https://doi.org/10.1137/1.9781611974768.21

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Concepts: Terrain, sort, Computer science, Flash flood, Flood myth, Quadtree, Merge (version control), Bottleneck, Computation, Algorithm, Parallel computing, Geography, Database, Cartography, Archaeology, Embedded system

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Cited by: 6

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Citations by year (recent): 2020: 2, 2019: 2, 2018: 1, 2017: 1

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