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A framework for automated anomaly detection in high frequency water-quality data from in situ sensors
2019
128 citations
Journal Article
green Open Access
Field-Weighted Citation Impact:
6.97
A framework for automated anomaly detection in high frequency water-quality data from in situ sensors | Researchclopedia
Catherine Neelamraju
·
Queensland Department of Environment and Science
Jennifer Strauss
·
Queensland Department of Environment and Science
Priyanga Dilini Talagala
·
ARC Centre of Excellence for Mathematical and Statistical Frontiers
Ryan Turner
·
Queensland Department of Environment and Science
Kerrie Mengersen
·
Queensland University of Technology
Erin E. Peterson
·
Queensland University of Technology