Knowledge discovery by means of inductive methods in wastewater treatment plant data

Authors:
Joaquim Comas;Saso Dzeroski;Karina Gibert;Ignasi R.-Roda;Miquel Sànchez‐Marrè
Affiliations:
Chemical and Environmental Engineering Laboratory (LEQUIA), University of Girona, Campus de Montilivi, E‐17071 Girona, Catalonia, Spain E‐mail: quim@lequia.udg.es, ignasi@l ...;Department of Intelligent Systems, Jozef Stefan Institute, Jamova 39, SI‐1000 Ljubljana, Slovenia E‐mail: Saso.Dzeroski@ijs.si;Department of Statistics and Operation Research, Technical University of Catalonia, C. Pau Gargallo, 5, E‐08028 Barcelona, Catalonia, Spain E‐mail: karina@eio.upc.es;Chemical and Environmental Engineering Laboratory (LEQUIA), University of Girona, Campus de Montilivi, E‐17071 Girona, Catalonia, Spain E‐mail: quim@lequia.udg.es, ignasi@l ...;(Corresponding author) Artificial Intelligence Section, Department of Software, Technical University of Catalonia, Campus Nord‐Edifici C5, E‐08034 Barcelona, Catalonia, Spain E‐mail ...
Venue:
AI Communications
Year:
2001

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Abstract

Artificial intelligence techniques, including machine learning methods, and statistical techniques have shown promising results as decision support tools, because of their capabilities of knowledge discovery, heuristic reasoning and working with uncertain and qualitative information. Wastewater treatment plants are complex environmental processes that are difficult to manage and control. This paper discusses the qualitative and quantitative performance of several machine learning and statistical methods to discover knowledge patterns in data. The methods are tested and compared on a wastewater treatment data set. The methods used are: induction of decision trees, two different techniques of rule induction and two memory‐based learning methods: instance‐based learning and case‐based learning. All the knowledge patterns discovered by the different methods are compared in terms of predictive accuracy, the number of attributes and examples used, and the meaningful‐ness to domain experts.