Combining expert knowledge and local data for improved service life modeling of water supply networks

The presented approach aims to overcome the scarce data problem in service life modeling of water networks by combining subjective expert knowledge and local replacement data. A procedure to elicit imprecise quantile estimates of survival functions from experts, considering common cognitive biases, was developed and applied. The individual expert priors of the parameters of the service life distribution are obtained by regression over the stated distribution quantiles and aggregated into a single prior distribution. Furthermore, a likelihood function for the commonly encountered censored and truncated pipe replacement data is formulated. The suitability of the suggested Bayesian approach based on elicitation data from eight experts and real network data is demonstrated. Robust parameter estimates could be derived in data situations where frequentist maximum likelihood estimation is unsatisfactory, and to show how the consideration of imprecision and in-between-variance of experts improves posterior inference.

► This approach improves estimation of water network service life under scarce data.
► Elicitation of imprecise quantiles of survival functions from experts is possible.
► A likelihood function for censored and truncated replacement data is formulated.
► Between-experts and single-expert variance are important sources of information.
► The local management strategy is important for correct pipe lifetime modeling.