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

Neal Andrew Barton; Stephen Henry Hallett; Simon Richard Jude; Trung Hieu Tran

doi:10.1038/s41545-022-00165-2

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

Neal Andrew Barton, Stephen Henry Hallett^*, Simon Richard Jude, Trung Hieu Tran

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Pipe 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.

Original language	English
Number of pages	11
Journal	npj Clean Water
Volume	5
Issue number	22
DOIs	https://doi.org/10.1038/s41545-022-00165-2
Publication status	Published - 17 Jun 2022

Bibliographical note

Copyright © The Authors(s) 2022. This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.

Access to Document

10.1038/s41545-022-00165-2Licence: CC BY 4.0

Predicting the risk of pipe failure using gradient boosted decision trees and weighted risk analysis
Copyright © The Authors(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.
Final published version, 2.39 MBLicence: CC BY 4.0

Cite this

@article{b35cdd36ce324cf7a2910cb381d50edf,

title = "Predicting the risk of pipe failure using gradient boosted decision trees and weighted risk analysis",

abstract = "Pipe 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.",

author = "Barton, {Neal Andrew} and Hallett, {Stephen Henry} and Jude, {Simon Richard} and Tran, {Trung Hieu}",

note = "Copyright {\textcopyright} The Authors(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article{\textquoteright}s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article{\textquoteright}s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.",

year = "2022",

month = jun,

day = "17",

doi = "10.1038/s41545-022-00165-2",

language = "English",

volume = "5",

number = "22",

}

TY - JOUR

T1 - Predicting the risk of pipe failure using gradient boosted decision trees and weighted risk analysis

AU - Barton, Neal Andrew

AU - Hallett, Stephen Henry

AU - Jude, Simon Richard

AU - Tran, Trung Hieu

N1 - Copyright © The Authors(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.

PY - 2022/6/17

Y1 - 2022/6/17

N2 - Pipe 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.

AB - Pipe 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.

UR - https://www.nature.com/articles/s41545-022-00165-2

U2 - 10.1038/s41545-022-00165-2

DO - 10.1038/s41545-022-00165-2

M3 - Article

SN - 2059-7037

VL - 5

JO - npj Clean Water

JF - npj Clean Water

IS - 22

ER -

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

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this