Bayesian Model Averaging for the Prediction of Water Main Failure for Small to Large Canadian Municipalities

Water utilities often rely on water main failure prediction model for developing preventive or proactive repair and replacement action program. Due to inherent uncertainties in modeling, it is challenging to understand the water main failure processes and to predict the failure effectively. In this study, Bayesian model averaging (BMA) method is presented to identify the influential covariates and to predict the failure rates of water mains considering model uncertainties. To accredit the proposed model, it is implemented to predict the failure of pipes of the water distribution network of the City of Kelowna, BC and Greater Vernon Water, BC, Canada. Results indicate that the proposed BMA approach capture the effect of the potential explanatory variables more effectively through the posterior probabilities in contrast to that of the p-value given by the classical regression analysis. Moreover, BMA approach perform better compare to classical regression analysis when limited pipe failure data is available.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author