Supporting the decision-making process in environmental monitoring systems with knowledge discovery techniques
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 involved in an air quality monitoring network, as recorded measurement validation and estimation of missing and erroneous values, are addressed through the exploitation of data mining techniques. Exhaustive experiments with real world data, resulted trustworthy predictive models, capable to support 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 involved in an EMS.
I. N. Athanasiadis, P. A. Mitkas, Supporting the decision-making process in environmental monitoring systems with knowledge discovery techniques, Knowledge Discovery for Environmental Management - Knowledge-based Services for the Public Sector Symposium, vol. Workshop III, pg. 1-12, 2004, KDnet.
You might also enjoy (View all publications)
- Introducing digital twins to agriculture
- Location-specific vs location-agnostic machine learning metamodels for predicting pasture nitrogen response rate
- Machine learning for large-scale crop yield forecasting