An approach to water supply clusters by semi-supervised learning

Modelling for Environment's Sake: Proceedings of the 5th Biennial Conference of the International Environmental Modelling and Software Society, iEMSs 2010

Volumn 3, Issue , 2010, Pages 1925-1932

  Herrera, M ^a   Canu, S ^b   Karatzoglou, A ^b   Perez Garcia R ^a   Izquierdo, J ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^b University of Rouen   (France)

Author keywords

Clustering; Decision support systems; Graph partitioning; Kernel methods; Water supply

Indexed keywords

ADJACENCY MATRICES; CLUSTERING; COMPLEX PROBLEMS; DISTRIBUTION OF WATER; GRAPH PARTITIONING; KERNEL METHODS; SEMI-SUPERVISED LEARNING; STRATEGIC OPTIONS; SUPPLY CONSTRAINTS; SUPPLY NETWORKS; TECHNICAL MANAGEMENT; WATER DISTRIBUTIONS; WATER SUPPLY NETWORKS;

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; DECISION SUPPORT SYSTEMS; STRUCTURAL OPTIMIZATION; SUPERVISED LEARNING; WATER SUPPLY SYSTEMS;

WATER SUPPLY;

EID: 84858690178     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. AVSA. Sectorización, 2009. Avalaible online http://www. aguasdevalencia.es/ last accessed in May 2009.
2. Covas, D. and H. Ramos. Practical methods for leakage control, detection and location in pressurised systems. In 13th International Conference on Pipeline Protection, 1999.
3. Hunaidi, O. Economic comparison of periodic acoustic surveys and dma-based. In Leakage 2005 Conference Proceedings, pages 322-336, 2005.
4. Hunaidi, O. and K. Brothers. Optimum size of district metered areas. In Water Loss Specialist Conference, International Water Association, pages 57-66, 2007.
5. IWA. District Metered Areas: Guidance Notes. IWA Eds., 2007.
6. Izquierdo, J., M. Herrera, I. Montalvo, and R. Pérez. Agent-based division of water ddistribution systems into district metered areas. In Proceedings of the 4th International Conference on Software and Data Technologies, ICSOFT 2009, 2009.
7. Kamvar, S., D. Klein, and C. Manning. Spectral learning. In In IJCAI, pages 561-566, 2003.
8. Kondor, R. I. and J. Lafferty. Diffusion kernels on graphs and other discrete input spaces. In Proceedings of the 19th International Conference on Machine Learning, pages 315-322. C. Sammut and A. Hofmann, editors, 2002.
9. Kulis, B., S. Basu, I. Dhillon, and R. Mooney. Semi-supervised graph clustering: a kernel approach. In ICML'05: Proceedings of the 22nd international conference on Machine Learning, pages 457-464. ACM, 2005. (Pubitemid 43183366)
10. Ng, A. Y., M. I. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems 14, pages 849-856. MIT Press, 2001.
11. Rossman, L. EPANET-User's Manual. United States Environmental Protection Agency (EPA), 2000.
12. Scholkopf, B. and A. J. Smola. Learning with kernels. MIT Press, 2002.
13. Shi, J. and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22:888-905, 2000.
14. Smola, A. J. and R. I. Kondor. Kernels and regularization on graph. In Proceedings of the 16th Annual Conference on Computational Learning Theory and the 7th Kernel Workshop, 2003.
15. Tzatchkov, V., V. Alcocer-Yamanaka, and V. Bourguett-Ortíz. Graph theory based algorithms for water distribution network sectorization projects. In 8th Annual Water Distribution Systems Analysis Symposium, 2006.
16. UKWIR. A Manual of DMA Practice. UKWIR Eds., London, 1999.
17. Vejmelka, M. Spectral graph clustering. In Seminar z Umele Inteligence, 2009.
18. Yu, S. X. and J. Shi. Segmentation given partial grouping constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2):173-183, 2004.
19. Zhu, X., J. Kandola, J. Lafferty, and Z. Ghahramani. Graph kernels by spectral transforms. MIT Press, 2006.