Predicting recreational water quality and public health safety in urban estuaries using Bayesian Networks

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dc.contributor.authorLloyd, S.
dc.contributor.authorCarvajal, G.
dc.contributor.authorCampey, M.
dc.contributor.authorTaylor, N.
dc.contributor.authorOsmond, P.
dc.contributor.authorRoser, D.
dc.contributor.authorKhan, S.
dc.date.accessioned2024-09-11T15:21:55Z
dc.date.available2024-09-11T15:21:55Z
dc.date.issued2024-05
dc.descriptionTEXTO COMPLETO EN INGLÉS
dc.description.abstractTo support the reactivation of urban rivers and estuaries for bathing while ensuring public safety, it is critical to have access to real-time information on microbial water quality and associated health risks. Predictive modelling can provide this information, though challenges concerning the optimal size of training data, model transferability, and communication of uncertainty still need attention. Further, urban estuaries undergo distinctive hydrological variations requiring tailored modelling approaches. This study assessed the use of Bayesian Networks (BNs) for the prediction of enterococci exceedances and extrapolation of health risks at planned bathing sites in an urban estuary in Sydney, Australia. The transferability of network structures between sites was assessed. Models were validated using a novel application of the k-fold walk-forward validation procedure and further tested using independent compliance and event-based sampling datasets. Learning curves indicated the model's sensitivity reached a minimum performance threshold of 0.8 once training data included ≥ 400 observations. It was demonstrated that Semi-Naïve BN structures can be transferred while maintaining stable predictive performance. In all sites, salinity and solar exposure had the greatest influence on Posterior Probability Distributions (PPDs), when combined with antecedent rainfall. The BNs provided a novel and transparent framework to quantify and visualise enterococci, stormwater impact, health risks, and associated uncertainty under varying environmental conditions. This study has advanced the application of BNs in predicting recreational water quality and providing decision support in urban estuarine settings, proposed for bathing, where uncertainty is high.
dc.description.urihttps://www-sciencedirect-com.recursosbiblioteca.unab.cl/science/article/pii/S0043135424002215
dc.identifier.citationWater Research, Volume 254 , 1 May 2024, 121319
dc.identifier.doihttps://doi.org/10.1016/j.watres.2024.121319
dc.identifier.issn1879-2448
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/60083
dc.language.isoen
dc.publisherElsevier
dc.rights.licenseAttribution 4.0 International
dc.subjectPredictive model
dc.subjectBayesian network
dc.subjectEnterococci
dc.subjectEstuary
dc.subjectRecreational water quality
dc.titlePredicting recreational water quality and public health safety in urban estuaries using Bayesian Networks
dc.typeArtículo
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