Prediction of pipe failures in water supply networks using logistic regression and support vector classification
•Prediction of >85% of pipe failures using both algorithms, LR and SVC.•Excellent abilities to avoid false classifications (AUCs).•Importance of training set balance in binary classification problems. Companies in charge of water supply networks are making a huge effort to optimally plan the annu...
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| Published in: | Reliability engineering & system safety Vol. 196; p. 106754 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.04.2020
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| ISSN: | 0951-8320, 1879-0836 |
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| Abstract | •Prediction of >85% of pipe failures using both algorithms, LR and SVC.•Excellent abilities to avoid false classifications (AUCs).•Importance of training set balance in binary classification problems.
Companies in charge of water supply networks are making a huge effort to optimally plan the annual replacements of pipes. This would save costs, enable a higher quality of service and a sustainable management of infrastructure.
This study presents a methodology to predict pipe failures in water supply networks. Logistic regression and support vector classification are chosen as predictive systems. Both provide a failure probability associated with each sample which is increasingly required by companies that manage these infrastructures. Furthermore, several pre-processing techniques that seek to improve the accuracy of predictions are addressed.
The proposed methodology is illustrated with the real case of a Spanish city. This is an extensive water supply network whose recorded data contains 4,393 pipe failures. The results obtained state that the number of unexpected failures might be significantly reduced. Around 30% of failures could have been prevented by replacing only 3% of the network's pipes per year, which is a realistic and feasible option.
As a future line of research, the objective must be to develop a global tool that incorporates the failure probability and its consequence, generating the optimal pipe replacement plan. |
|---|---|
| AbstractList | •Prediction of >85% of pipe failures using both algorithms, LR and SVC.•Excellent abilities to avoid false classifications (AUCs).•Importance of training set balance in binary classification problems.
Companies in charge of water supply networks are making a huge effort to optimally plan the annual replacements of pipes. This would save costs, enable a higher quality of service and a sustainable management of infrastructure.
This study presents a methodology to predict pipe failures in water supply networks. Logistic regression and support vector classification are chosen as predictive systems. Both provide a failure probability associated with each sample which is increasingly required by companies that manage these infrastructures. Furthermore, several pre-processing techniques that seek to improve the accuracy of predictions are addressed.
The proposed methodology is illustrated with the real case of a Spanish city. This is an extensive water supply network whose recorded data contains 4,393 pipe failures. The results obtained state that the number of unexpected failures might be significantly reduced. Around 30% of failures could have been prevented by replacing only 3% of the network's pipes per year, which is a realistic and feasible option.
As a future line of research, the objective must be to develop a global tool that incorporates the failure probability and its consequence, generating the optimal pipe replacement plan. Companies in charge of water supply networks are making a huge effort to optimally plan the annual replacements of pipes. This would save costs, enable a higher quality of service and a sustainable management of infrastructure. This study presents a methodology to predict pipe failures in water supply networks. Logistic regression and support vector classification are chosen as predictive systems. Both provide a failure probability associated with each sample which is increasingly required by companies that manage these infrastructures. Furthermore, several pre-processing techniques that seek to improve the accuracy of predictions are addressed. The proposed methodology is illustrated with the real case of a Spanish city. This is an extensive water supply network whose recorded data contains 4,393 pipe failures. The results obtained state that the number of unexpected failures might be significantly reduced. Around 30% of failures could have been prevented by replacing only 3% of the network's pipes per year, which is a realistic and feasible option. As a future line of research, the objective must be to develop a global tool that incorporates the failure probability and its consequence, generating the optimal pipe replacement plan. |
| ArticleNumber | 106754 |
| Author | Cortés, Pablo Onieva, Luis Robles-Velasco, Alicia Muñuzuri, Jesús |
| Author_xml | – sequence: 1 givenname: Alicia surname: Robles-Velasco fullname: Robles-Velasco, Alicia email: arobles2@us.es organization: Dpto. de Organización Industrial y Gestión de Empresas II. ETSI. Universidad de Sevilla. C/ Camino de los Descubrimientos S/N, 41092 Sevilla (Spain) – sequence: 2 givenname: Pablo surname: Cortés fullname: Cortés, Pablo organization: Dpto. de Organización Industrial y Gestión de Empresas II. ETSI. Universidad de Sevilla. C/ Camino de los Descubrimientos S/N, 41092 Sevilla (Spain) – sequence: 3 givenname: Jesús surname: Muñuzuri fullname: Muñuzuri, Jesús organization: Dpto. de Organización Industrial y Gestión de Empresas II. ETSI. Universidad de Sevilla. C/ Camino de los Descubrimientos S/N, 41092 Sevilla (Spain) – sequence: 4 givenname: Luis surname: Onieva fullname: Onieva, Luis organization: Dpto. de Organización Industrial y Gestión de Empresas II. ETSI. Universidad de Sevilla. C/ Camino de los Descubrimientos S/N, 41092 Sevilla (Spain) |
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| Keywords | Logistic regression Support vector classification Pipe failures Predictive algorithms Water supply networks |
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| Snippet | •Prediction of >85% of pipe failures using both algorithms, LR and SVC.•Excellent abilities to avoid false classifications (AUCs).•Importance of training set... Companies in charge of water supply networks are making a huge effort to optimally plan the annual replacements of pipes. This would save costs, enable a... |
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| SubjectTerms | Classification Failure Logistic regression Networks Optimization Pipe failures Pipes Predictive algorithms Reliability engineering Statistical analysis Support vector classification Sustainability management Water shortages Water supply Water supply networks Water supply systems |
| Title | Prediction of pipe failures in water supply networks using logistic regression and support vector classification |
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