Integrating Bayesian Linear Regression with Ordered Weighted Averaging: Uncertainty Analysis for Predicting Water Main Failures

Abstract

Water distribution networks (WDNs) are among the most important and expensive municipal infrastructure assets that are vital to public health. Municipal authorities strive for implementing preventive (or proactive) programs rather than corrective (or reactive) programs. The ability to predict the failure of pipes in WDNs is vital in the proactive investment planning of replacement and rehabilitation strategies. However, due to inherent uncertainties in data and modeling, WDN failure prediction is challenging. To improve understanding of water main failure processes, accurate quantification of uncertainty is necessary. The research reported in this paper presents a comparative evaluation of the prediction accuracy of normal multiple linear regression and Bayesian regression models using water mains failure data/information from the City of Calgary. Results indicate that Bayesian regression models provide better predicted response and handle the uncertainty more accurately than normal regression model.