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Open AccessArticle

Sewer Life Span Prediction: Comparison of Methods and Assessment of the Sample Impact on the Results

1
Department of Built Environment, Aalto University, P.O. Box 15200, 00076 Aalto, Finland
2
Department of Mathematics and Systems Analysis, Aalto University, P.O. Box 11100, 00076 Aalto, Finland
*
Author to whom correspondence should be addressed.
Water 2019, 11(12), 2657; https://doi.org/10.3390/w11122657
Received: 19 November 2019 / Revised: 11 December 2019 / Accepted: 12 December 2019 / Published: 16 December 2019
(This article belongs to the Section Urban Water Management)
Survival models can support the estimation of the resources needed for future renovations of sewer systems. They are particularly useful, when a large share of network will need renovation. This paper studies modelling sewer deterioration in a context, where data are available for pipes selected for inspections due to suspected or experienced poor condition. We compare the random survival forest and the Weibull regression for modelling survival and find that both methods yield similar results, but the random survival forest performs slightly better. We propose a method for estimating the range in which the actual network survival curve lies. We conclude that in order to reach reliable results, a life span model needs to be constructed based on a random sample of pipes, which are then consecutively inspected and in addition, censoring and left truncation need to be accounted for. The inspection data applied in this paper had been collected with the aim of finding pipes in poor condition in the network. As a result, the data were biased towards poor condition and unrepresentative in terms of pipe ages. View Full-Text
Keywords: sewer life span; survival models; deterioration modelling sewer life span; survival models; deterioration modelling
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MDPI and ACS Style

Laakso, T.; Kokkonen, T.; Mellin, I.; Vahala, R. Sewer Life Span Prediction: Comparison of Methods and Assessment of the Sample Impact on the Results. Water 2019, 11, 2657.

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