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)
- Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in Embu County, Kenya
- Reuse of process-based models: automatic transformation into many programming languages and simulation platforms
- Advances and New Trends in Environmental Informatics. Digital Twins for Sustainability