skip to main content
10.1145/3274895.3274980acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
research-article
Best Paper

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

Published:06 November 2018Publication 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

References

  1. PK Agarwal, L Arge, and K Yi. 2006. I/O-efficient Batched Union-Find and its Applications to Terrain Analysis. In Proc. 22nd Annu. Sympos. on Comp. Geom. 167--176. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. L Arge, JS Chase, P Halpin, L Toma, JS Vitter, D Urban, and R Wickremesinghe. 2003. Efficient flow computation on massive grid terrain datasets. GeoInformatica 7, 4 (2003), 283--313. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. L Arge, M Rav, S Raza, and M Revsbæk. 2017. I/O-Efficient Event Based Depression Flood Risk. In Proc. 19th Workshop on Algorithm Engineering and Experiments. 259--269.Google ScholarGoogle ScholarCross RefCross Ref
  4. L Arge and M Revsbæk. 2009. I/O-efficient Contour Tree Simplification. In Intl. Sympos. on Algos. and Computation. 1155--1165. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. L Arge, M Revsbæk, and N Zeh. 2010. I/O-efficient computation of water flow across a terrain. In Proc. 26th Annu. Sympos. on Comp. Geom. 403--412. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. H Carr, J Snoeyink, and U Axen. 2003. Computing contour trees in all dimensions. Comp. Geom. 24, 2 (2003), 75--94. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. H Carr, J Snoeyink, and M Panne. 2010. Flexible isosurfaces: Simplifying and displaying scalar topology using the contour tree. Comp. Geom. 43, 1 (2010), 42--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A Danner, T Mølhave, K Yi, PK Agarwal, L Arge, and H Mitásová. 2007. Terra-Stream: from elevation data to watershed hierarchies. In Proc. 15th Annu. ACM Intl. Sympos. on Advances in GIS. 28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. H Edelsbrunner, J Harer, and A Zomorodian. 2001. Hierarchical Morse complexes for piecewise linear 2-manifolds. In Proc. 17th Annu. Sympos. Comp. Geom. 70--79. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Indiana Spatial Data Portal. 2013. Indiana Orthophotography (RGBI), LiDAR and Elevation. http://gis.iu.edu/datasetInfo/statewide/in_2011.php.Google ScholarGoogle Scholar
  11. SK Jenson and JO Domingue. 1988. Extracting topographic structure from digital elevation data for geographic information system analysis. Photogrammetric Engineering and Remote Sensing 54, 11 (1988), 1593--1600.Google ScholarGoogle Scholar
  12. M Kreveld, R Oostrum, C Bajaj, V Pascucci, and D Schikore. 1997. Contour trees and small seed sets for isosurface traversal. In Proc. 13th Annu. Sympos. on Comp. Geom. 212--220. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Y Liu and J Snoeyink. 2005. Flooding triangulated terrain. In Proc. 11th Intl. Sympos. on Spatial Data Handling. 137--148.Google ScholarGoogle ScholarCross RefCross Ref
  14. JF O'Callaghan and DM Mark. 1984. The extraction of drainage networks from digital elevation data. Computer Vision, Graphics, and Image Processing 28, 3 (1984), 323--344.Google ScholarGoogle ScholarCross RefCross Ref
  15. PFBJ Quinn, K Beven, P Chevallier, and O Planchon. 1991. The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models. Hydrological processes 5, 1 (1991), 59--79.Google ScholarGoogle Scholar
  16. M Rav, A Lowe, and PK Agarwal. 2017. Flood Risk Analysis on Terrains. In Proc. of the 25th ACM SIGSPATIAL Int. Conference on Advances in GIS. ACM, 36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. SP Tarasov and MN Vyalyi. 1998. Construction of contour trees in 3D in O(n log n) steps. In Proc. 14th Annu. Sympos. on Comp. Geom. 68--75. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

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

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      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

      Copyright © 2018 ACM

      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].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 6 November 2018

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      SIGSPATIAL '18 Paper Acceptance Rate30of150submissions,20%Overall Acceptance Rate220of1,116submissions,20%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader