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Article

Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters

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Laboratoire de Mathématiques et de Leurs Applications de Pau Fédération MIRA, UMR CNRS 5142, Université de Pau et des Pays de l’Adour, 64600 Anglet, France
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Department of Mathematics and Statistics, Macquarie University, Sydney, NSW 2109, Australia
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National Ecological Observatory Network, Battelle Boulder, Boulder, CO 80301, USA
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Institute of Arctic Biology and Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
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School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia
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ARC Centre of Excellence for Mathematics and Statistical Frontiers, Melbourne, VIC 3000, Australia
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Peterson Consulting, Brisbane, QLD 4000, Australia
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Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia
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Biosciences and Food Technology Discipline, School of Science, RMIT University, Bundoora, VIC 3083, Australia
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Author to whom correspondence should be addressed.
Academic Editors: Ethel Marian Scott, Craig Anderson, Claire A. Miller and Ruth A. O’Donnell
Int. J. Environ. Res. Public Health 2021, 18(23), 12803; https://doi.org/10.3390/ijerph182312803
Received: 27 October 2021 / Revised: 26 November 2021 / Accepted: 2 December 2021 / Published: 4 December 2021
(This article belongs to the Special Issue Statistical Advances in Environmental Sciences)
In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems. View Full-Text
Keywords: anomaly correction; generalised additive model (GAM); missing data reconstruction; remote sensing; water quality anomaly correction; generalised additive model (GAM); missing data reconstruction; remote sensing; water quality
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MDPI and ACS Style

Kermorvant, C.; Liquet, B.; Litt, G.; Jones, J.B.; Mengersen, K.; Peterson, E.E.; Hyndman, R.J.; Leigh, C. Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters. Int. J. Environ. Res. Public Health 2021, 18, 12803. https://doi.org/10.3390/ijerph182312803

AMA Style

Kermorvant C, Liquet B, Litt G, Jones JB, Mengersen K, Peterson EE, Hyndman RJ, Leigh C. Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters. International Journal of Environmental Research and Public Health. 2021; 18(23):12803. https://doi.org/10.3390/ijerph182312803

Chicago/Turabian Style

Kermorvant, Claire, Benoit Liquet, Guy Litt, Jeremy B. Jones, Kerrie Mengersen, Erin E. Peterson, Rob J. Hyndman, and Catherine Leigh. 2021. "Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters" International Journal of Environmental Research and Public Health 18, no. 23: 12803. https://doi.org/10.3390/ijerph182312803

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