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Flood-risk analysis on terrains under the multiflow-direction model

Published: 06 November 2018 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 paper 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 to more accurately represent flooding events.
We present three main results: First, we present an O(nm)-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; here m is the number of sinks in Σ. Second, we present a O(n log n)-time algorithm for preprocessing Σ into a linear-size data structure for answering point-flood queries: given a rain distribution R, a volume of rain ψ falling according to R, and point qΣ, determine whether q will be flooded. A point-flood query can be answered in O(nk) time, where k is the number of maximal depressions in Σ containing the query point q. Alternately, we can preprocess Σ in O(n log n + nm) time into an O(nm)-size data structure so that a point-flood query can be answered in O(|R|k+k2) time, where |R| is the number of vertices in R with positive rain fall. Finally, we present algorithms for answering a flood-time query: given a rain distribution R and a point qΣ, 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, that runs in O(nk + k2(log(n + logα)ρ))-time, where ρ is a variable on the terrain which depends on the ratio between depression volumes.
We implemented our terrain-flooding algorithm and tested its efficacy and efficiency on real terrains

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    cover image ACM Conferences
    SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
    November 2018
    655 pages
    ISBN:9781450358897
    DOI:10.1145/3274895
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 06 November 2018

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

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    SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;
    Overall Acceptance Rate 257 of 1,238 submissions, 21%

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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)Flood-risk analysis on terrainsCommunications of the ACM10.1145/341041363:9(94-102)Online publication date: 21-Aug-2020
    • (2019)Flood-Risk Analysis on Terrains under the Multiflow-Direction ModelACM Transactions on Spatial Algorithms and Systems10.1145/33407075:4(1-27)Online publication date: 18-Sep-2019
    • (2019)Flood Risk Analysis on TerrainsACM Transactions on Spatial Algorithms and Systems10.1145/32954595:1(1-31)Online publication date: 5-Jun-2019

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