Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review
Abstract There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-10-01
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| Series: | Applied Water Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13201-023-02013-1 |
| _version_ | 1797630024730279936 |
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| author | Edwar Forero-Ortiz Eduardo Martinez-Gomariz Marti Sanchez-Juny Jaume Cardus Gonzalez Fernando Cucchietti Ferran Baque Viader Miquel Sarrias Monton |
| author_facet | Edwar Forero-Ortiz Eduardo Martinez-Gomariz Marti Sanchez-Juny Jaume Cardus Gonzalez Fernando Cucchietti Ferran Baque Viader Miquel Sarrias Monton |
| author_sort | Edwar Forero-Ortiz |
| collection | DOAJ |
| description | Abstract There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive capabilities of water distribution network models. Extensive research has been conducted to explore the role of explanatory variables in optimizing model outputs. However, the underlying mechanisms of incorporating explanatory variable data into the models still need to be better understood. This review aims to expand our understanding of explanatory variables and their relationship with existing models through a comprehensive investigation of the explanatory variables employed in models over the past 15 years. The review underscores the importance of obtaining a substantial and reliable dataset directly from Water Utilities databases. Only with a sizeable dataset containing high-quality data can we better understand how all the variables interact, a crucial prerequisite before assessing the performance of pipe failure rate prediction models. |
| first_indexed | 2024-03-11T11:02:13Z |
| format | Article |
| id | doaj.art-7375bdc20d42419bbefe9b53d66e632e |
| institution | Directory Open Access Journal |
| issn | 2190-5487 2190-5495 |
| language | English |
| last_indexed | 2024-03-11T11:02:13Z |
| publishDate | 2023-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj.art-7375bdc20d42419bbefe9b53d66e632e2023-11-12T12:26:08ZengSpringerOpenApplied Water Science2190-54872190-54952023-10-01131114110.1007/s13201-023-02013-1Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature reviewEdwar Forero-Ortiz0Eduardo Martinez-Gomariz1Marti Sanchez-Juny2Jaume Cardus Gonzalez3Fernando Cucchietti4Ferran Baque Viader5Miquel Sarrias Monton6Aigües de Barcelona, Empresa Metropolitana de la Gestió del Cicle Integral de l’AiguaAigües de Barcelona, Empresa Metropolitana de la Gestió del Cicle Integral de l’AiguaFlumen Research Institute, Universitat Politècnica de Catalunya - Centre Internacional de Mètodes Numèrics en EnginyeriaAigües de Barcelona, Empresa Metropolitana de la Gestió del Cicle Integral de l’AiguaBarcelona Supercomputing CenterAMB, Àrea Metropolitana de Barcelona, Servei de Supervisió de Concessions, Direcció de Serveis del Cicle de l’AiguaWater Technology CentreAbstract There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive capabilities of water distribution network models. Extensive research has been conducted to explore the role of explanatory variables in optimizing model outputs. However, the underlying mechanisms of incorporating explanatory variable data into the models still need to be better understood. This review aims to expand our understanding of explanatory variables and their relationship with existing models through a comprehensive investigation of the explanatory variables employed in models over the past 15 years. The review underscores the importance of obtaining a substantial and reliable dataset directly from Water Utilities databases. Only with a sizeable dataset containing high-quality data can we better understand how all the variables interact, a crucial prerequisite before assessing the performance of pipe failure rate prediction models.https://doi.org/10.1007/s13201-023-02013-1Water distributionWater networkWater pipeline failureInfrastructure asset managementPipe burst rate predictionPipe renewal |
| spellingShingle | Edwar Forero-Ortiz Eduardo Martinez-Gomariz Marti Sanchez-Juny Jaume Cardus Gonzalez Fernando Cucchietti Ferran Baque Viader Miquel Sarrias Monton Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review Applied Water Science Water distribution Water network Water pipeline failure Infrastructure asset management Pipe burst rate prediction Pipe renewal |
| title | Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review |
| title_full | Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review |
| title_fullStr | Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review |
| title_full_unstemmed | Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review |
| title_short | Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review |
| title_sort | models and explanatory variables in modelling failure for drinking water pipes to support asset management a mixed literature review |
| topic | Water distribution Water network Water pipeline failure Infrastructure asset management Pipe burst rate prediction Pipe renewal |
| url | https://doi.org/10.1007/s13201-023-02013-1 |
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