Skip to main content
View expanded cover

Transactions on Rough Sets I pp 356–374Cite as

Application of Rough Sets to Environmental Engineering Models

  • Conference paper

Part of the Lecture Notes in Computer Science book series (TRS,volume 3100)

Abstract

Rough Sets is a method of dealing with domains characterised by inconsistent and incomplete information. We apply this method to the problem of Environmental Engineering modelling. The solid waste management problem, in particular, is the subject of our analysis.

Modelling large engineering problems is difficult because of the volume of information processed and the number of modelling decisions being made. In many cases, a chicken and the egg problem presents itself when modelling new or one-of-a-kind systems: The model is needed to gain the knowledge necessary for constructing the model.

The generally accepted solution is to iteratively verify the importance of a parameter or model change until a concise model is created which appropriately supports the decision making process. We improve on this process by using Rough Sets to actively search for simplifying assumptions of the model and validate the process using a municipal solid waste management case.

Keywords

  • Decision Attribute
  • Information Table
  • Municipal Solid Waste Management
  • Conditional Attribute
  • Deterministic Rule

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-540-27794-1_17
  • Chapter length: 19 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-27794-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.00
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Warren, R.H.: Domain knowledge acquisition from solid waste management models using rough sets. Master’s thesis, University of Regina, Faculty of Graduate Studies, Regina, Saskachewan, Canada (2000)

    Google Scholar 

  2. Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough sets. Communications of the ACM 38(11), 89–95 (1995)

    CrossRef  Google Scholar 

  3. Wong, S., Ziarko, W.: Algorithm for inductive learning. Bulletin of Polish Academy of Sciences 34(5), 271–276 (1986)

    MathSciNet  Google Scholar 

  4. Wong, S.K.M., Ziarko, W., Ye, R.L.: Comparison of rough-set and statistical methods in inductive learning. International Journal of Man-Machine Studies 24, 53–72 (1986)

    MATH  CrossRef  Google Scholar 

  5. Wakulicz-Deja, A., Boryczka, M., Paszak, P.: Discretization of continuous attributes on decision system in mitochondrial encephalomyopathies. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 481–490. Springer, Heidelberg (1998)

    CrossRef  Google Scholar 

  6. Dunphy, J.R., Salcedo, J.J., Murphy, K.S.: Intelligent generation of candidate sets for genetic algorithms in very large search spaces. In: RSFDGrC 2003, vol. 2639, pp. 453–457. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

  7. Dougherty, J., Al.: Supervised and unsupervised discretization of continuous features. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 194–202 (1995)

    Google Scholar 

  8. Huang, G.H.: Grey Mathematical Programming and its application to Waste Management Planning under Uncertainty. PhD thesis, McMaster University, Department of Civil Engineering, Hamilton, Ontario, Canada (1994)

    Google Scholar 

  9. Huang, G.H., Baetz, B.W., Patry, G.G.: Grey fuzzy dynamic programming: Application to municipal solid waste management planning problems. Civil Engineering Systems 11, 43–73 (1994)

    CrossRef  Google Scholar 

  10. Huang, G.H., Baetz, B.W., Patry, G.G.: Grey fuzzy integer programming: an application to regional waste management planning uncer uncertainty. Socio-Economic Planning Science 29(1), 17 (1995)

    CrossRef  Google Scholar 

  11. Wu, X.: Inexact nonlinear programming and its application to solid waste management and planning. Master’s thesis, University of Regina, Faculty of Engineering, Regina, Saskachewan, Canada (1997)

    Google Scholar 

  12. Wisniewski, T.J., Al.: Simplex Java Applet. Optimization Technology Center, Argonne National Labratory and Northwestern University (January 1997), http://wwwfp.mcs.anl.gov/otc/Guide/CaseStudies/simplex/applet/SimplexTool.html

  13. T.P. development team: PostgreSQL. Open Source Software (2003)

    Google Scholar 

  14. Shannon, C.E.: The Mathematical Theory of Communication. The University of Illinois Press, Illinois (1949)

    MATH  Google Scholar 

  15. Huang, G.H., Baetz, B.W., Patry, G.G.: Grey fuzzy dynamic programming: application to municipal solid waste management planning problems. Civil Engineering Systems 11, 43–74 (1994)

    CrossRef  Google Scholar 

  16. Liang, A., Maguire, M., Johnson, J.: Rough set based webct learning. In: Lu, H., Zhou, A. (eds.) WAIM 2000. LNCS, vol. 1846, pp. 425–436. Springer, Heidelberg (2000)

    CrossRef  Google Scholar 

  17. Johnson, J., Yang, X.D., Hu, Q.: Word sense disambiguation in the rough. In: Proc. Fourth Symposium on Natural Language Processing SNLP, pp. 206–223 (2000)

    Google Scholar 

  18. Johnson, J., Li, H.: Rough set approach for deadlock detection in petri nets. In: Proc. International Conference on Artificial Intelligence ICAI, pp. 1435–1439 (2000)

    Google Scholar 

  19. Johnson, J.: Specification from examples. In: Proc. International Conference on Advances in Infrastructure for Electronic Business, Science, and Education on the Internet, L’Aquila, Italy, July 31-August 6 (2000)

    Google Scholar 

  20. Johnson, J.A.: Rough scheduling. In: Proceedings of the 5th Joint Conference in Information Science JCIS 2000, pp. 162–165 (2000)

    Google Scholar 

  21. Johnson, J.: Rough mereology for industrial design. In: Polkowski, L., Skowron, A. (eds.) Rough Sets and Current Trends in Computing, pp. 553–556. Springer, Heidelberg (1998)

    CrossRef  Google Scholar 

  22. Johnson, J.: Rough scheduling. In: Proc. 5th Joint Conference in Information Science JCIS 2000, vol. 1, pp. 162–165 (2000)

    Google Scholar 

  23. Abraham, S., Kathail, V., Beitrich, B.: Meld scheduling: A technique for relaxing scheduling constraints. International Journal of Parallel Programming 26(4), 349–381 (1998)

    CrossRef  Google Scholar 

  24. Barták, R.: Theory and practice of constraint propagation. In: 3rd Workshop on Constraint Programming for Decision and Control CPDC 2001, Gliwice, Poland, pp. 7–14 (2001)

    Google Scholar 

  25. Lesaint, D.: Inferring constraint types in constraint programming. In: Eighth International Conference on Principles and Practice of Constraint Programming, Ithaca, NY (2002)

    Google Scholar 

  26. Komorowski, J., Polkowski, L., Skowron, A.: Rough sets: A tutorial (2002), http://www.folli.uva.nl/CD/1999/library/coursematerial/Skowron/Skowron.pdf

  27. Skowron, A.: Approximate reasoning by agents in distributed environments. In: IAT 2001, Maebashi, Japan (2001), http://kis.maebashi-it.ac.jp/iat01/pdf/iatinvited/IAT01_SKO_SLE.pdf

  28. Shen, W., Ghenniwa, H. (eds.): Journal of Intelligent Manufacturing: Special Issue on Agent-Based Manufacturing Process Planning and Scheduling. Kluwer Academic Publishers, Dordrecht (to appear)

    Google Scholar 

  29. Shen, L., Tay, F.E.H., Qu, L., Shen, Y.: Fault diagnosis using rough sets theory. Computers in Industry 43(1), 61–72 (2000)

    CrossRef  Google Scholar 

  30. Lingrass, P.: Unsupervised rough set classification using genetic algorithms. Journal of Intelligent Information Systems 16(3), 215–228 (2001)

    CrossRef  Google Scholar 

  31. Tay, F.E.H., Shen, L.: Economic and financial prediction using rough sets model. European Journal of Operational Research 141(3), 643–661 (2002)

    CrossRef  Google Scholar 

  32. Chartrand, M., Johnson, J.: Network mining for managing a broadband network. In: Rzevski, G., Adey, P., Russell, D. (eds.) Data Mining 2003. Computational Mechanics, Southampton (2003) (to appear)

    Google Scholar 

  33. Hui, Z., Johnson, J., Bin, W., Gui, L.Z.: Control rules generating method based on rough sets. Journal of Computer Engineering and Application 39(13), 98–100 (2003); written in Chinese, English translation available from julia@cs.laurentian.ca

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Robert H. Warren

  2. Laurentian University, Sudbury, ON, P3E 2C6, Canada

    Julia A. Johnson

  3. University of Regina, Regina, SK, S4S 0A2, Canada

    Gordon H. Huang

Authors
  1. Robert H. Warren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julia A. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gordon H. Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron & Marcin S. Szczuka & 

  3. Institute of Computer Science, Polish Academy of Sciences, 01–237, Warsaw, Poland

    Jerzy W. Grzymała-Busse

  4. Multimedia Systems Department, Gdańsk University of Technology, Narutowicza 11/12, 80-952, Gdańsk, Poland

    Bożena Kostek

  5. Department of Mathematical and Computer Sciences, San Diego State University, 5500 Campanile Drive, CA 92182, San Diego, USA

    Roman W. Świniarski

Rights and permissions

Reprints and Permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Warren, R.H., Johnson, J.A., Huang, G.H. (2004). Application of Rough Sets to Environmental Engineering Models. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Świniarski, R.W., Szczuka, M.S. (eds) Transactions on Rough Sets I. Lecture Notes in Computer Science, vol 3100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27794-1_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-27794-1_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22374-0

  • Online ISBN: 978-3-540-27794-1

  • eBook Packages: Springer Book Archive