Sewer Condition Prediction and Analysis of Explanatory Factors

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39 Citations (Scopus)
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Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors affect it. We apply the random forest algorithm to model pipe condition and assess the variable importance using the Boruta algorithm. We analyse the impact of predictor variables on poor condition using partial dependence plots, which are a valuable technique for this purpose. The results can be used in screening pipes for future inspections and provide insight into the dynamics between predictor variables and poor condition
Original languageEnglish
Article number1239
Pages (from-to)1-17
Number of pages17
Issue number9
Publication statusPublished - 13 Sept 2018
MoE publication typeA1 Journal article-refereed


  • Boruta algorithm
  • logistic regression
  • partial dependence plot
  • random forest
  • sewer condition
  • variable selection


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