Skip to main content
SpringerLink
Log in

Application of Machine Learning in Water Distribution Networks Assisted by Domain Experts

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

The human-assisted application of machine learning techniques in the domain of water distribution networks is presented, corresponding to a research work done in the context of the European Esprit project WATERNET. One part of this project is a learning system that intends to capture knowledge from historic information collected during the operation of water distribution networks. The captured knowledge is expected to contribute to the improvement of the operation of the network. Presented ideas correspond to the first development phase of the learning system, focusing specially on the adopted methodology. The interactions between different classes of human experts and the learning system are also discussed. Finally some experimental results are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Afsarmanesh, H., Benabdelkader, A., and Hertzberger. O.: A flexible approach to information sharing in water industries, in: Proc. of CIT'98 – Internat. Conf. on Trends in Information Technology, Bhubaneswar, India, 21–23 December 1998, McGraw-Hill, pp. 135–142.

  2. Afsarmanesh, H., Camarinha-Matos, L. M., and Martinelli, F. J.: Federated knowledge integration and machine learning in water distribution networks, in: L. M. Camarinha-Matos(ed.), Re-engineering for Sustainable Industrial Production, Chapman & Hall, London, 1997, pp. 121–140.

    Google Scholar 

  3. Brachman, R. J. and Annad, T.: The process of knowledge discovery in databases, in: U. M. Fayyad et al. (eds), Advances in Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA/MIT Press, Cambridge, MA, 1996, pp. 37–57.

    Google Scholar 

  4. Brodley, C. E. and Smyth, P.: Applying classification algorithms in practice, Statist. Comput. 7 (1997), 45–56.

    Google Scholar 

  5. Camarinha-Matos, L. M. and Martinelli, F. J.: Application of machine learning in water distribution networks: An initial study, in: R. Engels et al. (eds), Proc. of the Workshop on Machine Learning Application in the Real World, Methodological Aspects and Implications, Nashville, TN, 1997, pp. 48–57.

  6. Camarinha-Matos, L. M. and Martinelli, F. J.: Application of machine learning in water distribution networks: An initial study, in: L. M. Camarinha-Matos, H. Afsarmanesh, and V. Marik (eds), Intelligent Systems for Manufacturing: Multi-Agent Systems and Virtual Organizations, Kluwer Academic, Dordrecht, 1998, pp. 121–136.

    Google Scholar 

  7. Camarinha-Matos, L. M. and Martinelli, F. J.: A system for data mining in water distribution networks, in: Proc. of CIT '98 – Internat. Conf. on Trends in Information Technology, Bhubaneswar, India, 21–23 December 1998, McGraw-Hill, New York, pp. 118–125.

    Google Scholar 

  8. European Water Task Force: Freshwater – A challenge for research and innovation – A concerned European response, European Comission Report; EUR 18098 EN, July 1998.

  9. Langley, P.: Challenges for the application of machine learning, in: R. Engels et al. (eds), Proc. of the Workshop on Machine Learning Applications in the Real World, Methodological Aspects and Implications, Nashville, TN, 1997, pp. 16–18.

  10. Loucks, D. P. and Costa, J. R.: Computer-aided decision support in water resource planning and management, in: D. P. Loucks and J. R. Costa (eds), Decision Support Systems: Water Resources Planning, Springer, Berlin, 1991, pp. 3–41.

    Google Scholar 

  11. Larsson, J. E.: Diagnosis based on explicit means-end models, Artificial Intelligence 80 (1996), 29–93.

    Google Scholar 

  12. Lind, M.: Modelling goals and functions of complex industrial plants, Appl. Artificial Intell. 8 (1994), 259–283.

    Google Scholar 

  13. Mitchell, T. M.: Machine Learning, McGraw-Hill, New York, 1997.

    Google Scholar 

  14. Quevedo, J. et al.: A contribution to the interactive dispatching of water distribution system, in: Internat. Symposium on AI, Expert Systems and Languages in Modeling and Simulation, Barcelona, 1987, pp. 41–46.

  15. Quinlan, J. R.: C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993.

    Google Scholar 

  16. Wirth, R. et al.: Towards process-oriented tool support for KDD, in: Proc. of the 1st European Symposium on Principles of DataMining and Knowledge Discovery, Trondheim, Norway, June 1997.

Download references

Author information

Authors and Affiliations

  1. Faculty of Sciences and Technology Quinta da Torre, New University of Lisbon and Uninova, 2825 Monte Caparica, Portugal

    Luis M. Camarinha-Matos & Fernando J. Martinelli

Authors
  1. Luis M. Camarinha-Matos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando J. Martinelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Camarinha-Matos, L.M., Martinelli, F.J. Application of Machine Learning in Water Distribution Networks Assisted by Domain Experts. Journal of Intelligent and Robotic Systems 26, 325–352 (1999). https://doi.org/10.1023/A:1008193214890

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008193214890

Search

Navigation