Identification of More Characteristic Dynamic Patterns in a WWTP by ClBR×E

  • Karina Gibert
  • Gustavo Rodríguez Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


In this work, advances in the design of an hybrid methodology that combines tools of Artificial Intelligence and Statistics to extract a model of explicit knowledge are presented in regards to the dynamics of a Wastewater Treatment Plant. Our line of work is based in the development of methodologies of AI & Stats to solve problems of Knowledge Discovery of Data where an integral vision of the pre-process, the automatic interpretation of results and the explicit production of knowledge play a role as important as the analysis itself. In our current work the identification of more characteristic dynamic patterns is approached with Clustering Based on Rules by States, which consists in the analysis of the stages that the water treatment moves through, to integrate the knowledge discovered from each subprocess into a unique model of global operation of the phenomenon.


clustering rules dynamics states wastewater 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Karina Gibert
    • 1
  • Gustavo Rodríguez Silva
    • 1
  1. 1.Department of Statistics and Operations ResearchTechnical University of CataloniaBarcelonaSpain

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