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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Veröffentlicht in:	Reliability engineering & system safety Jg. 196; S. 106754
Hauptverfasser:	Robles-Velasco, Alicia, Cortés, Pablo, Muñuzuri, Jesús, Onieva, Luis
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Barking Elsevier Ltd 01.04.2020 Elsevier BV
Schlagworte:	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 Logistic regression Support vector classification Pipe failures Predictive algorithms Water supply networks
ISSN:	0951-8320, 1879-0836
Online-Zugang:	Volltext
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