Sewer Condition Prediction and Analysis of Explanatory Factors

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2018-09-13

Department

Department of Built Environment
Department of Mathematics and Systems Analysis

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Language

en

Pages

17

Series

Water, Volume 10, issue 9, pp. 1-17

Abstract

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

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Keywords

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

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DOI

10.3390/w10091239

Citation

Laakso, T, Kokkonen, T, Mellin, I & Vahala, R 2018, ' Sewer Condition Prediction and Analysis of Explanatory Factors ', Water, vol. 10, no. 9, 1239, pp. 1-17 . https://doi.org/10.3390/w10091239

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https://urn.fi/URN:NBN:fi:aalto-201809215142

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[article-cris] Insinööritieteiden korkeakoulu / ENG