Sewer Condition Prediction and Analysis of Explanatory Factors

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2018-09-13
Major/Subject
Mcode
Degree programme
Language
en
Pages
17
1-17
Series
WATER, Volume 2018, issue 10
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
Description
Keywords
Boruta algorithm, logistic regression, partial dependence plot, random forest, sewer condition, variable selection
Other note
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