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Open AccessArticle

Sewer Condition Prediction and Analysis of Explanatory Factors

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Department of Built Environment, Aalto University, P.O. Box 15200, 00076 Aalto, Finland
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Department of Mathematics and Systems Analysis, Aalto University, P.O. Box 11100, 00076 Aalto, Finland
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Author to whom correspondence should be addressed.
Water 2018, 10(9), 1239; https://doi.org/10.3390/w10091239
Received: 24 July 2018 / Revised: 1 September 2018 / Accepted: 11 September 2018 / Published: 13 September 2018
(This article belongs to the Section Urban Water Management)
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. View Full-Text
Keywords: Boruta algorithm; logistic regression; partial dependence plot; random forest; sewer condition; variable selection Boruta algorithm; logistic regression; partial dependence plot; random forest; sewer condition; variable selection
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MDPI and ACS Style

Laakso, T.; Kokkonen, T.; Mellin, I.; Vahala, R. Sewer Condition Prediction and Analysis of Explanatory Factors. Water 2018, 10, 1239.

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