Predicting the risk of pipe failure using gradient boosted decision trees and weighted risk analysis

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dc.contributor.authorBarton, Neal Andrew
dc.contributor.authorHallett, Stephen H.
dc.contributor.authorJude, Simon R.
dc.contributor.authorTran, Trung Hieu
dc.date.accessioned2022-07-19T15:30:26Z
dc.date.available2022-07-19T15:30:26Z
dc.date.issued2022-06-17
dc.description.abstractPipe failure prediction models are essential for informing proactive management decisions. This study aims to establish a reliable prediction model returning the probability of pipe failure using a gradient boosted tree model, and a specific segmentation and grouping of pipes on a 1 km grid that associates localised characteristics. The model is applied to an extensive UK network with approximately 40,000 km of pipeline and a 14-year failure history. The model was evaluated using the Receiver Operator Curve and Area Under the Curve (0.89), briers score (0.007) and Mathews Correlation Coefficient (0.27) for accuracy, indicating acceptable predictions. A weighted risk analysis is used to identify the consequence of a pipe failure and provide a graphical representation of high-risk pipes for decision makers. The weighted risk analysis provided an important step to understanding the consequences of the predicted failure. The model can be used directly in strategic planning, which sets long-term key decisions regarding maintenance and potential replacement of pipes.en_UK
dc.identifier.citationBarton NA, Hallett SH, Jude SR, Tran TH. (2022) Predicting the risk of pipe failure using gradient boosted decision trees and weighted risk analysis. npj Clean Water, Volume 5, June 2022, Article number 22en_UK
dc.identifier.issn2059-7037
dc.identifier.urihttps://doi.org/10.1038/s41545-022-00165-2
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18194
dc.language.isoenen_UK
dc.publisherNature Publishing Groupen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titlePredicting the risk of pipe failure using gradient boosted decision trees and weighted risk analysisen_UK
dc.typeArticleen_UK

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