Detecting Technical Anomalies in High-Frequency Water-Quality Data Using Artificial Neural Networks

Journal: Environmental Science and Technology

Published: 2020-08-28

DOI: 10.1021/acs.est.0c04069

Affiliations: 8

Authors: 7

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Institutions Share
Laboratoire de Mathématiques et de leurs Applications de Pau (LMAP), France 0.33
ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights (ACEMS), Australia 0.21
QUT Institute for Future Environments (IFE), Australia 0.10
QUT School of Mathematical Sciences, Australia 0.10
Department of Mathematics and Statistics, Macquarie University, Australia 0.07
Mediterranean Institute of Oceanography (MIO), France 0.07
Institut Pluridisciplinaire de Recherche Appliquée (IPRA), France 0.07
School of Science, RMIT University, Australia 0.05

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