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.
Download full text in pdf format
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)
- Learning long-term crop management strategies with CyclesGym
- Rapid turnover of sensor data to genetic evaluation for dairy cows in the cloud
- Mixing process-based and data-driven approaches in yield prediction