Prediction of pipe failures in water supply networks using logistic regression and support vector classification-Reference-Cited by-同舟云学术

Prediction of pipe failures in water supply networks using logistic regression and support vector classification

Published:2020-04 Issue: Volume:196 Page:106754
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Robles-Velasco Alicia,Cortés Pablo,Muñuzuri Jesús,Onieva Luis

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference49 articles.

1. AEAS, “XIV estudio nacional de suministro de agua potable y saneamiento en españa,” 2016. [Online]. Available:http://www.aeas.es/servlet/mgc?pg=ListNews&ret=next&news_id=1249&areaCode=publicarea&newsCategory=Noticias. [Accessed: 03-Jan-2019].

2. State-of-the-technology review on water pipe condition, deterioration and failure rate prediction models!;St. Clair;Urban Water J,2012

3. Failure analysis and assessment on the exemplary water supply network;Pietrucha-Urbanik;Eng Fail Anal,2015

4. Risk-based prioritization of water main failure using fuzzy synthetic evaluation technique;Al-Zahrani;J Water Supply Res Technol—AQUA,2016

5. Non-hydraulic factors analysis of pipe burst in water distribution systems;Li;Procedia Eng,2015

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