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

Lisa Scholten*, Andreas Scheidegger, Peter Reichert, Max Maurer

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

34 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalEnvironmental Modelling and Software
Publication statusPublished - 1 Apr 2013
Externally publishedYes


  • Bayesian inference
  • Expert aggregation
  • Expert knowledge elicitation
  • Scarce data
  • Service life modeling
  • Water supply network


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