Supporting the Decision-Making Process in Environmental Monitoring Systems with Knowledge Discovery Techniques
Ioannis N. Athanasiadis, Pericles A. Mitkas · 2004
Abstract. In this paper an empirical approach for supporting the decision making process involved in an Environmental Management System (EMS) that monitors air quality and triggers air quality alerts is presented. Data uncertainty problems associated with an air quality monitoring network, such as measurement validation and estimation of missing or erroneous values, are addressed through the exploitation of data mining techniques. Exhaustive experiments with real world data have produced trustworthy predictive models, capable of supporting the decision-making process. The outstanding performance of the induced predictive models indicate the added value of this approach for supporting the decision making process in an EMS. 1.