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A Software Package for Automated Partitioning of Catchments

  • Ralf Denzer
  • Tobias Kalmes
  • Udo Gauer
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 448)

Abstract

This paper reports about a software package which has been developed to automatically partition hydrological networks (catchments) into clusters of similar size. Such clustering is useful for parallel simulation of catchments on distributed computing systems and is typically based on heuristic graph algorithms.

There have been a few approaches to automatically partition catchments, but literature research indicates that there seems to be no systematic investigation of the usefulness of different graph algorithms for catchment partitioning over a reasonable number of real world data sets. Our study aims at making a step in this direction.

The paper describes the software package, which has been implemented in Java, its pluggable architecture, and initial experiments using the European catchment dataset ECRINS. The paper presents work in progress.

Keywords

parallel simulation hydrological network graph clustering 

References

  1. 1.
    Grübsch, M., David, O.: How to Divide a Catchment to Conquer Its Parallel Processing,an Efficient Algorithm for the Partitioning of Water Catchments. Mathematical and Computer Modelling 33, 723–731 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Wang, H., et al.: A common parallel computing framework for modeling hydrological processes of river basins. Parallel Computing 37, 302–315 (2011)CrossRefGoogle Scholar
  3. 3.
    Yalew, S., et al.: Distributed computation of large scale SWAT models on the Grid. Environmental Modelling & Software 41, 223–230 (2013)CrossRefGoogle Scholar
  4. 4.
    Denzer, R., Fitch, P., Athanasiadis, I.N., Ames, D.P.: Parallel simulation of environmental phenomena. International Congress on Environmental Modelling and Software 2014 (2014), http://www.iemss.org/sites/iemss2014/proceedings.php ISBN: 978-88-9035-744-2
  5. 5.
    EEA, EEA Catchments and Rivers Network System, ECRINSv1.1, EEA Technical report No 7/2012, European Environment Agency (2012) ISSN 1725-2237 Google Scholar
  6. 6.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Analysis and MachineIntelligence 24, S. 881–S. 892 (2002), doi:10.1109/TPAMI.2002.1017616 (Abgerufen am April 24, 2009)Google Scholar
  7. 7.
    King, A.D.: Graph Clustering with Restricted Neighbourhood Search, Thesis (2004), http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.129.2497&rep=rep1&type=pdf
  8. 8.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm, http://snap.stanford.edu/class/cs224w-readings/ng01spectralcluster.pdf

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Ralf Denzer
    • 1
  • Tobias Kalmes
    • 1
  • Udo Gauer
    • 1
  1. 1.Environmental Informatics Group (EIG)SaarbrückenGermany

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