Data Mining Methods for Quality Assurance in an Environmental Monitoring Network
The paper presents a system architecture that employs data mining techniques for ensuring quality assurance in an environmental monitoring network. We investigate how data mining techniques can be incorporated in the quality assurance decision making process. As prior expert decisions are available, we demonstrate that expert knowledge can be effectively extracted and reused for reproducing human experts decisions on new data. The framework is demonstrated for the Saudi Aramco air quality monitoring network and yields trustworthy behavior on historical data. A variety of data-mining algorithms was evaluated, resulting to an average predictive accuracy of over 80 while best models reached 90% of correct decisions.
I. N. Athanasiadis, A. E. Rizzoli, D. Beard, Data Mining Methods for Quality Assurance in an Environmental Monitoring Network, 20th Intl Conf on Artificial Neural Networks (ICANN 2010), Lecture Notes in Computer Science, vol. 6354, pg. 451-456, 2010, Springer Verlag, doi:10.1007/978-3-642-15825-4_60.
You might also enjoy (View all publications)
- A weakly supervised framework for high-resolution crop yield forecasts
- Learning latent representations for operational nitrogen response rate prediction
- Simulation-assisted machine learning for operational digital twins