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

    Author(s)
    Rodriguez-Perez, J
    Leigh, C
    Liquet, B
    Kermorvant, C
    Peterson, E
    Sous, D
    Mengersen, K
    Griffith University Author(s)
    Leigh, Catherine
    Year published
    2020
    Metadata
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    Abstract
    Anomaly detection (AD) in high-volume environmental data requires one to tackle a series of challenges associated with the typical low frequency of anomalous events, the broad-range of possible anomaly types, and local nonstationary environmental conditions, suggesting the need for flexible statistical methods that are able to cope with unbalanced high-volume data problems. Here, we aimed to detect anomalies caused by technical errors in water-quality (turbidity and conductivity) data collected by automated in situ sensors deployed in contrasting riverine and estuarine environments. We first applied a range of artificial ...
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    Anomaly detection (AD) in high-volume environmental data requires one to tackle a series of challenges associated with the typical low frequency of anomalous events, the broad-range of possible anomaly types, and local nonstationary environmental conditions, suggesting the need for flexible statistical methods that are able to cope with unbalanced high-volume data problems. Here, we aimed to detect anomalies caused by technical errors in water-quality (turbidity and conductivity) data collected by automated in situ sensors deployed in contrasting riverine and estuarine environments. We first applied a range of artificial neural networks that differed in both learning method and hyperparameter values, then calibrated models using a Bayesian multiobjective optimization procedure, and selected and evaluated the "best" model for each water-quality variable, environment, and anomaly type. We found that semi-supervised classification was better able to detect sudden spikes, sudden shifts, and small sudden spikes, whereas supervised classification had higher accuracy for predicting long-term anomalies associated with drifts and periods of otherwise unexplained high variability.
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    Journal Title
    Environmental Science & Technology
    Volume
    54
    Issue
    21
    DOI
    https://doi.org/10.1021/acs.est.0c04069
    Subject
    Marine geoscience
    Environmental sciences
    Publication URI
    http://hdl.handle.net/10072/399570
    Collection
    • Journal articles

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