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Sewer Condition Prediction and Analysis of Explanatory Factors

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Laakso, Tuija
dc.contributor.author Kokkonen, Teemu
dc.contributor.author Mellin, Ilkka
dc.contributor.author Vahala, Riku
dc.date.accessioned 2018-09-21T09:49:07Z
dc.date.available 2018-09-21T09:49:07Z
dc.date.issued 2018-09-13
dc.identifier.citation Laakso , T , Kokkonen , T , Mellin , I & Vahala , R 2018 , ' Sewer Condition Prediction and Analysis of Explanatory Factors ' , WATER , vol. 10 , no. 9 , 1239 , pp. 1-17 . https://doi.org/10.3390/w10091239 en
dc.identifier.issn 2073-4441
dc.identifier.other PURE UUID: 34a89431-b97e-4b8e-9af0-a5773962c66a
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/34a89431-b97e-4b8e-9af0-a5773962c66a
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85053276734&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/27969906/water_10_01239.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/34047
dc.description.abstract Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors affect it. We apply the random forest algorithm to model pipe condition and assess the variable importance using the Boruta algorithm. We analyse the impact of predictor variables on poor condition using partial dependence plots, which are a valuable technique for this purpose. The results can be used in screening pipes for future inspections and provide insight into the dynamics between predictor variables and poor condition en
dc.format.extent 17
dc.format.extent 1-17
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries WATER en
dc.relation.ispartofseries Volume 2018, issue 10 en
dc.rights openAccess en
dc.title Sewer Condition Prediction and Analysis of Explanatory Factors en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Water and Environmental Eng.
dc.contributor.department Department of Mathematics and Systems Analysis
dc.contributor.department Department of Built Environment en
dc.subject.keyword Boruta algorithm
dc.subject.keyword logistic regression
dc.subject.keyword partial dependence plot
dc.subject.keyword random forest
dc.subject.keyword sewer condition
dc.subject.keyword variable selection
dc.identifier.urn URN:NBN:fi:aalto-201809215142
dc.identifier.doi 10.3390/w10091239
dc.type.version publishedVersion

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