Knowledge discovery by means of inductive methods in wastewater treatment plant data
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.
knowledge discovery wastewater treatment machine learning decision trees rule induction instance-based learning case-based learning statistical clustering and rule induction