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Urban Water Journal Volume 17, 2020 - Issue 6
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Research Articles

A comparative study of statistical and machine learning methods to infer causes of pipe breaks in water supply networks

Charalampos KonstantinouDepartment of Civil and Environmental Engineering, Imperial College London, London, UKCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-4662-5327
ORCID Icon &
Ivan StoianovDepartment of Civil and Environmental Engineering, Imperial College London, London, UKORCID Iconhttps://orcid.org/0000-0003-0600-2894
ORCID Icon
Pages 534-548 | Received 14 Oct 2019, Accepted 17 Jul 2020, Published online: 05 Aug 2020

Citations (18)

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Read on this site (2)

Ahmad Ravanbakhsh, Mehdi Momeni & Amir Robati. (2023) A comparison between multivariate adaptive regression splines and regressive convolution neural network with support vector regression for pipe burst rate prediction on limited dataset. Urban Water Journal 20:10, pages 1813-1823.
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Ahmed A. Abokifa & Lina Sela. (2023) Integrating spatial clustering with predictive modeling of pipe failures in water distribution systems. Urban Water Journal 20:4, pages 465-476.
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Articles from other publishers (16)

Shamsuddin Daulat, Marius Møller Rokstad, Stian Bruaset, Jeroen Langeveld & Franz Tscheikner-Gratl. (2024) Evaluating the generalizability and transferability of water distribution deterioration models. Reliability Engineering & System Safety 241, pages 109611.
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Carlos Jara-Arriagada & Ivan Stoianov. (2024) Pressure-induced fatigue failures in cast iron water supply pipes. Engineering Failure Analysis 155, pages 107731.
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Edwar Forero-Ortiz, Eduardo Martinez-Gomariz, Marti Sanchez-Juny, Jaume Cardus Gonzalez, Fernando Cucchietti, Ferran Baque Viader & Miquel Sarrias Monton. (2023) Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review. Applied Water Science 13:11.
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Charalampos Konstantinou & Yuze Wang. (2023) Unlocking the Potential of Microbially Induced Calcium Carbonate Precipitation (MICP) for Hydrological Applications: A Review of Opportunities, Challenges, and Environmental Considerations. Hydrology 10:9, pages 178.
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Hongfang Lu, Zhao-Dong XuXulei ZangDongmin XiTom IseleyJohn C. Matthews & Niannian Wang. (2023) Leveraging Machine Learning for Pipeline Condition Assessment. Journal of Pipeline Systems Engineering and Practice 14:3.
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Lorenzo Vergni & Francesca Todisco. (2023) A Random Forest Machine Learning Approach for the Identification and Quantification of Erosive Events. Water 15:12, pages 2225.
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Ridwan Taiwo, Mohamed El Amine Ben Seghier & Tarek Zayed. (2023) Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes. Water Resources Research 59:4.
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Thomas Ying‐Jeh Chen, Eric Wang, Nicole Pasch & Amin Ganjidoost. (2023) Multi‐objective optimization models for the renewal planning of multiple asset classes . AWWA Water Science 5:2.
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Hongfang Lu, Zhao-Dong Xu, Tom Iseley, Haoyan Peng & Lingdi FuHongfang Lu, Zhao-Dong Xu, Tom Iseley, Haoyan Peng & Lingdi Fu. 2023. Pipeline Inspection and Health Monitoring Technology. Pipeline Inspection and Health Monitoring Technology 117 212 .
Charalampos Konstantinou & Giovanna Biscontin. (2022) Experimental Investigation of the Effects of Porosity, Hydraulic Conductivity, Strength, and Flow Rate on Fluid Flow in Weakly Cemented Bio-Treated Sands. Hydrology 9:11, pages 190.
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Ylenia Casali, Nazli Yonca Aydin & Tina Comes. (2022) Machine learning for spatial analyses in urban areas: a scoping review. Sustainable Cities and Society 85, pages 104050.
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Neal Andrew Barton, Stephen Henry Hallett, Simon Richard Jude & Trung Hieu Tran. (2022) Predicting the risk of pipe failure using gradient boosted decision trees and weighted risk analysis. npj Clean Water 5:1.
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Thomas Ying-Jeh ChenGreta VladeanuSepideh YazdekhastiCraig Michael Daly. (2022) Performance Evaluation of Pipe Break Machine Learning Models Using Datasets from Multiple Utilities. Journal of Infrastructure Systems 28:2.
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N. A. Barton, S. H. Hallett, S. R. Jude & T. H. Tran. (2022) An evolution of statistical pipe failure models for drinking water networks: a targeted review. Water Supply 22:4, pages 3784-3813.
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Amin Ganjidoost, Greta Vladeanu & Craig Michael Daly. (2022) Leveraging risk and data analytics for sustainable management of buried water infrastructure. AWWA Water Science 4:2.
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N. A. Barton, S. H. Hallett & S. R. Jude. (2022) The challenges of predicting pipe failures in clean water networks: a view from current practice. Water Supply 22:1, pages 527-541.
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