Metadata about: The maintenance and renewal of water mains demand substantial financial investments, and direct inspection of all water mains in a distribution system is extremely expensive. Therefore, a cost effective break mitigation technique such as a failure forecasting model that allows one to predict the water mains failure likelihood, would reduce the negative social impact and the cost to serve. We introduce a semiparametric Bayesian model for pipeline failure forecasting. The model is centred on a nonparametric Gaussian Process Regression (GPR), and uses a parametric survival model to capture the long-term survival probability using domain knowledge. The parametric element in our model allows the inclusion of survival probability, while the nonparametric part allows us to handle covariates and to employ incomplete prior knowledge about pipe failures. We apply our model to the proactive maintenance problem using a real dataset from a water utility in Australia. The results demonstrate that, our model performs better than competing models such as Support Vector Regression, Poisson regression, Weibull, Gradient Boosting, and GPR, leading to substantial savings on reactive repairs and maintenance. Our water pipeline failure prediction models have been deployed in three states across Australia, and are being monitored by each water authority.

About: The maintenance and renewal of water mains demand substantial financial investments, and direct inspection of all water mains in a distribution system is extremely expensive. Therefore, a cost effective break mitigation technique such as a failure forecasting model that allows one to predict the water mains failure likelihood, would reduce the negative social impact and the cost to serve. We introduce a semiparametric Bayesian model for pipeline failure forecasting. The model is centred on a nonparametric Gaussian Process Regression (GPR), and uses a parametric survival model to capture the long-term survival probability using domain knowledge. The parametric element in our model allows the inclusion of survival probability, while the nonparametric part allows us to handle covariates and to employ incomplete prior knowledge about pipe failures. We apply our model to the proactive maintenance problem using a real dataset from a water utility in Australia. The results demonstrate that, our model performs better than competing models such as Support Vector Regression, Poisson regression, Weibull, Gradient Boosting, and GPR, leading to substantial savings on reactive repairs and maintenance. Our water pipeline failure prediction models have been deployed in three states across Australia, and are being monitored by each water authority.

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