Comparative Study of Three Stochastic Models for Prediction of Pipe Failures in Water Supply Systems

Abstract

The prediction of pipe failures in urban water systems is a complex process because the available failure records, originating in work orders, are often short and incomplete. To identify a robust and simple model with good failure prediction results using short data history, three existing models were compared in this study: the single-variate Poisson process, the Weibull accelerated lifetime model, and the linear-extended Yule process. This work also presents modifications to these models that enable them to produce more accurate predictions and overcome computational issues for practical software implementation. The three models, together with the improvements where applicable, were applied to water supply system data provided by a Portuguese water utility, and the results were comparatively analysed to assess the accuracy of each model. The Weibull accelerated lifetime model yielded the best results among the three models, accurately predicting failures and detecting pipes with high failure likelihood; however, it is based on Monte Carlo simulations, which can be time-consuming. The linear extended Yule process could also effectively detect pipes with higher failure likelihood; however, it presented a clear tendency to overestimate the number of future failures. The single-variate Poisson process is the simplest of the three models and produced failure prediction results of lower quality.