Data mining methods for quality assurance in an environmental monitoring network

Ioannis N. Athanasiadis, Andrea Emilio Rizzoli, Daniel W. Beard

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks – ICANN 2010
Subtitle of host publication20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part III
EditorsKonstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis
PublisherSpringer
Pages451-456
ISBN (Electronic)9783642158254
ISBN (Print)9783642158247
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event20th International Conference on Artificial Neural Networks, ICANN 2010 - Thessaloniki, Greece
Duration: 15 Sept 201018 Sept 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume6354
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Artificial Neural Networks, ICANN 2010
Country/TerritoryGreece
CityThessaloniki
Period15/09/1018/09/10

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