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  • Spatiotemporal prediction of Escherichia coli and Enterococci for the Commonwealth Games triathlon event using Bayesian Networks

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    Accepted Manuscript (AM)
    Author(s)
    Bertone, E
    Purandare, J
    Durand, B
    Griffith University Author(s)
    Bertone, Edoardo
    Year published
    2019
    Metadata
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    Abstract
    A number of Bayesian Networks were developed in order to nowcast and forecast, up to 4 days ahead and in different locations, the likelihood of water quality within the 2018 Commonwealth Games Triathlon swim course exceeding the critical limits for Enterococci and Escherichia coli. The models are data-driven, but the identification of potential inputs and optimal model structure was performed through the parallel contribution of several stakeholders and experts, consulted through workshops. The models, whose main nodes were discretised with a customised discretisation algorithm, were validated over a test set of data and ...
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    A number of Bayesian Networks were developed in order to nowcast and forecast, up to 4 days ahead and in different locations, the likelihood of water quality within the 2018 Commonwealth Games Triathlon swim course exceeding the critical limits for Enterococci and Escherichia coli. The models are data-driven, but the identification of potential inputs and optimal model structure was performed through the parallel contribution of several stakeholders and experts, consulted through workshops. The models, whose main nodes were discretised with a customised discretisation algorithm, were validated over a test set of data and deployed in real-time during the Commonwealth Games in support to a traditional water quality monitoring program. The proposed modelling framework proved to be cost-effective and less time-consuming than process-based models while still achieving high accuracy; in addition, the added value of a continuous stakeholder engagement guarantees a shared understanding of the model outputs and its future deployment.
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    Journal Title
    Marine Pollution Bulletin
    Volume
    146
    DOI
    https://doi.org/10.1016/j.marpolbul.2019.05.066
    Copyright Statement
    © 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Environmental assessment and monitoring
    Surface water quality processes and contaminated sediment assessment
    Biological oceanography
    Publication URI
    http://hdl.handle.net/10072/386422
    Collection
    • Journal articles

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