Exploration of the relationship between water main breaks and temperature covariates

References

• Ahn , J. C. 2005 . Predicting water pipe breaks using neural network . Water Science and Technology: Water Supply , 5 ( 3-4 ) : 159 – 172 .
   Google Scholar
• Bahmanyar , G. H. and Edil , T. B. Cold weather effects on underground pipeline failures. In . Proceedings of Conference on Pipelines in Adverse Environments II . Edited by: Pickell , Mark B. pp. 579 – 593 . San Diego, CA : ASCE .
   Google Scholar
• Constantine , A. G. and Darroch , J. N. 1993 . “ Pipeline reliability: stochastic models in engineering technology and management ” . In Proceedings of the Australia–Japan Workshop on Stochastic Models in Engineering, Technology and Management , Edited by: Osaki , S. and Murthy , D. N.P. 86 – 95 . Gold Coast , , Australia : World Scientific Publishing Co .
   Google Scholar
• Constantine , A. G. , Darroch , J. N. and Miller , R. 1996 . Predicting underground pipe failure . Water (Journal of Australian Water Works Association) , 23 ( 2 ) : 9 – 10 .
   Google Scholar
• Economou , T. , Kapelan , Z. and Bailey , T. A zero-inflated Bayesian model for the prediction of water pipebursts . Proceedings of the 10th International WaterDistribution System Analysis Conference . pp. 724 – 734 . South Africa. [CD-ROM]. Reston, VA: ASCE : Kruger National Park .
   Google Scholar
• Goodchild , C. W. , Rowson , T. C. and Engelhardt , M. O. 2009 . “ Making the earth move: modelling the impact ofclimate change on water pipeline serviceability. In ” . In Computing and Control in the Water Industry 2009: Integrating Water Systems , Edited by: Boxall , J. and Maksimovic , C. 807 – 811 . Sheffield, UK : University of Sheffield .
   Google Scholar
• Habibian , A. 1994 . Effect of temperature changes on water-main break . Journal Transportation Engineering , 120 ( 2 ) : 312 – 321 .
   Web of Science ®Google Scholar
• Hughes , D. 2011 . Continuous system acoustic monitoring – from start to repair , Vol. 74 , Denver , CO : Water Research Foundation .
   Google Scholar
• Kleiner , Y. and Rajani , B. 2003 . Forecasting variations and trends in water-main breaks . Journal of Infrastructure Systems , 8 ( 4 ) : 122 – 131 .
   Google Scholar
• Kleiner , Y. and Rajani , B. 2004 . Quantifying effectiveness of cathodic protection in water mains: theory . Journal of Infrastructure Systems , 10 ( 2 ) : 43 – 51 .
   Google Scholar
• Kleiner , Y. and Rajani , B. 2009 . “ I-WARP: individual water main renewal planner ” . In Computing and control in the water industry 2009: integrating water systems , Edited by: Boxall , J. and Maksimovic , C. 639 – 644 . Sheffield, UK : University of Sheffield .
   Google Scholar
• Lochbaum , B. S. 1993 . PSE&G develops models to predict main breaks . Pipeline and Gas Journal , 20 ( 9 ) : 20 – 27 .
   Google Scholar
• Newport , R. 1981 . Factors influencing the occurrence of bursts in iron water mains . Water Supply and Management , 3 : 274 – 278 .
   Google Scholar
• Røstum , J. 2000 . Statistical modelling of pipe failures in water networks , Trondheim : Norway . Thesis (PhD). Norwegian University of Science and Technology
   Google Scholar
• Sacluti , F. , Stanley , S. J. and Zhang , Q. Use of artificial neural networks to predict water distribution pipe breaks . In: Proceedings of the 51st Annual Conference of the Western Canada Water and Wastewater .
   Google Scholar
• Walski , T. M. and Pelliccia , A. 1982 . Economic analysis ofwater main breaks . Journal American Water Works Association , 74 ( 3 ) : 140 – 147 .
   Web of Science ®Google Scholar