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Flood-Risk Analysis on Terrains under the Multiflow-Direction Model

Published: 18 September 2019 Publication History

Abstract

An important problem in terrain analysis is modeling how water flows across a terrain and creates floods by filling up depressions. In this article, we study a number of flood-risk related problems: Given a terrain Σ, represented as a triangulated xy-monotone surface with n vertices, a rain distribution R, and a volume of rain ψ, determine which portions of Σ are flooded. We develop efficient algorithms for flood-risk analysis under the multiflow-directions (MFD) model, in which water at a point can flow along multiple downslope edges and which more accurately represent flooding events.
We present three main results: First, we present an O(n log n)-time algorithm to answer a terrain-flood query: if it rains a volume ψ according to a rain distribution R, determine what regions of Σ will be flooded. Second, we present a O(n log n + nm)-time algorithm for preprocessing Σ containing m sinks into a data structure of size O(nm) for answering point-flood queries: Given a rain distribution R, a volume of rain ψ falling according to R, and point q ∈ Σ, determine whether q will be flooded. A point-flood query can be answered in O(|R|k+k2) time, where k is the number of maximal depressions in Σ containing the query point q and |R| is the number of vertices in R with positive rainfall. Finally, we present algorithms for answering a flood-time query: given a rain distribution R and a point q ∈ Σ, determine the volume of rain that must fall before q is flooded. Assuming that the product of two k × k matrices can be computed in O(kω) time, we show that a flood-time query can be answered in O(nk + kω) time. We also give an α-approximation algorithm, for α > 1, which runs in O(n log n log α ρ)-time, where ρ is a variable on the terrain that depends on the ratio between depression volumes. We implemented our algorithms for computing terrain and point-flood queries as well as approximate flood-time queries. We tested the efficacy and efficiency of these algorithms on three real terrains of different types (urban, suburban, and mountainous.)

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Cited By

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  • (2023)1D and 2D Flow Routing on a TerrainACM Transactions on Spatial Algorithms and Systems10.1145/35396609:1(1-39)Online publication date: 12-Jan-2023
  • (2020)1D and 2D Flow Routing on a TerrainProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422269(5-14)Online publication date: 3-Nov-2020
  • (2020)Point Flood Query Based on Fast Binary Merge TreeProceedings of the 2020 3rd International Conference on Geoinformatics and Data Analysis10.1145/3397056.3397075(38-42)Online publication date: 15-Apr-2020

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  1. Flood-Risk Analysis on Terrains under the Multiflow-Direction Model

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    cover image ACM Transactions on Spatial Algorithms and Systems
    ACM Transactions on Spatial Algorithms and Systems  Volume 5, Issue 4
    December 2019
    164 pages
    ISSN:2374-0353
    EISSN:2374-0361
    DOI:10.1145/3361970
    Issue’s Table of Contents
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    Publication History

    Published: 18 September 2019
    Accepted: 01 June 2019
    Revised: 01 May 2019
    Received: 01 December 2018
    Published in TSAS Volume 5, Issue 4

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    Author Tags

    1. Terrains
    2. flood-risk analysis
    3. merge trees

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    • ARO
    • U.S.-Israel Binational Science Foundation
    • NSF

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    Cited By

    View all
    • (2023)1D and 2D Flow Routing on a TerrainACM Transactions on Spatial Algorithms and Systems10.1145/35396609:1(1-39)Online publication date: 12-Jan-2023
    • (2020)1D and 2D Flow Routing on a TerrainProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422269(5-14)Online publication date: 3-Nov-2020
    • (2020)Point Flood Query Based on Fast Binary Merge TreeProceedings of the 2020 3rd International Conference on Geoinformatics and Data Analysis10.1145/3397056.3397075(38-42)Online publication date: 15-Apr-2020

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