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dc.contributor.authorBrester, C
dc.contributor.authorRyzhikov, I
dc.contributor.authorSiponen, S
dc.contributor.authorJayaprakash, B
dc.contributor.authorIkonen, J
dc.contributor.authorPitkänen, T
dc.contributor.authorMiettinen, IT
dc.contributor.authorTorvinen, E
dc.contributor.authorKolehmainen, M
dc.date.accessioned2020-04-22T09:15:30Z
dc.date.available2020-04-22T09:15:30Z
dc.date.issued2020
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/8099
dc.description.abstractWaterborne disease outbreaks are a persistent and serious threat to public health according to reported incidents across the globe. Online drinking water quality monitoring technologies have evolved substantially and have become more accurate and accessible. However, using online measurements alone is unsuitable for detecting microbial regrowth, potentially including harmful species, ahead of time in the distribution systems. Alternatively, observational data could be collected periodically, e.g. once per week or once per month and it could include a representative set of variables: physicochemical water characteristics, disinfectant concentrations, and bacterial abundances, which would be a valuable source of knowledge for predictive modelling that aims to reveal pathogen-related threats. In this study, we utilised data collected from a pilot-scale drinking water distribution system. A data-driven random forest model was used for predictive modelling and was trained for nowcasting and forecasting abundances of bacterial groups. In all the experiments, we followed the realistic crossline scenario, which means that when training and testing the models the data is collected from different pipelines. In spite of the more accurate results of the nowcasting, the 1-week forecasting still provided accurate predictions of the most abundant bacteria, their rapid increase and decrease. In the future predictive modelling might be used as a tool in designing control measures for opportunistic pathogens which are able to multiply in the favourable conditions in drinking water distribution systems (DWDS). Eventually, the forecasting information will be able to produce practically helpful data for controlling the DWDS regrowth.
dc.language.isoenglanti
dc.publisherElsevier BV
dc.relation.ispartofseriesScience of the total environment
dc.relation.urihttp://dx.doi.org/10.1016/j.scitotenv.2020.137249
dc.rightsCC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectnowcasting
dc.subjectforecasting
dc.subjectbacterial abundance
dc.subjectwater pipeline
dc.subjectabsolute read count
dc.subjectrandom forest
dc.titlePotential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling
dc.description.versionfinal draft
dc.contributor.departmentYmpäristö- ja biotieteiden laitos / Toiminta
uef.solecris.id68786953en
dc.type.publicationTieteelliset aikakauslehtiartikkelit
dc.rights.accessrights© Elsevier B.V.
dc.relation.doi10.1016/j.scitotenv.2020.137249
dc.description.reviewstatuspeerReviewed
dc.publisher.countryAlankomaat
dc.relation.articlenumber137249
dc.relation.issn0048-9697
dc.relation.volume717
dc.rights.accesslevelopenAccess
dc.type.okmA1
uef.solecris.openaccessEi


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