Supporting decision making in urban wastewater systems using a knowledge-based approach

  • Authors:

  • Montse Aulinas;Juan Carlos Nieves;Ulises Cortés;Manel Poch

  • Affiliations:

  • Laboratory of Chemical and Environmental Engineering (LEQUIA), Science and Technology Park, University of Girona, Pic de Peguera 15, E17003 Girona, Spain and Knowledge Engineering and Machine Lear ...;Knowledge Engineering and Machine Learning Group (KEMLG), Software Department (LSI), Technical University of Catalonia, c/Jordi Girona 1-3, E08034 Barcelona, Spain;Knowledge Engineering and Machine Learning Group (KEMLG), Software Department (LSI), Technical University of Catalonia, c/Jordi Girona 1-3, E08034 Barcelona, Spain;Laboratory of Chemical and Environmental Engineering (LEQUIA), Science and Technology Park, University of Girona, Pic de Peguera 15, E17003 Girona, Spain and Institut Catalí de Recerca de l'A ...

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2011

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Abstract

The use of knowledge-based systems has been shown to be a suitable approach to support decision making in environmental systems. Capturing and managing the huge quantity of data/information that has to be considered is an intrinsic factor that makes environmental systems a sophisticated domain. Organizing this data in a naive way can impact the efficacy of any knowledge-based system. Another intrinsic factor is the variety of data sources, which can result in inconsistent, uncertain or incomplete knowledge bases when different data sources are considered. Accordingly, two central issues of a successful knowledge-based system are the organization of its knowledge base and the expressiveness of its specification language. In this paper, we introduce a stratified framework for structuring any environmental knowledge base. We will argue that a declarative specification language, such as Answer Set Programming, is expressive enough to capture environmental knowledge bases that are inconsistent, uncertain and incomplete. We also present an automata-based approach to manage actions in knowledge-based systems. By solving a use case, specifically the diagnosis of the safety of a particular industrial wastewater discharge in an urban wastewater system, we illustrate how to represent relevant abstractions to model related complex processes. We show that by using them it is also possible to automate the diagnosis process (in the present case, for example, to diagnose problems at a wastewater treatment plant and afterward in the river) and hence support the decision-making task.