Predicting sewer structural condition using hybrid machine learning algorithms

Predicting the structural condition of sewer pipes plays a vital role in the predictive maintenance of sewer pipes and renewal plans of many water utilities. This study explores the simultaneous utilization of physical and environmental features of sewer pipes in sewer structural condition predictio...

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Bibliographic Details
Published in:Urban water journal 2023-08, Vol.20 (7), p.882-896
Main Authors: Nguyen, L. V., Razak, S.
Format: Article
Language:English
Subjects:
Algorithms
Classifiers
Decision trees
Learning algorithms
Machine learning
Pipes
Predictive maintenance
Renewal
Sewer maintenance
sewer network
Sewer pipes
structural condition
Water utilities
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Summary:Predicting the structural condition of sewer pipes plays a vital role in the predictive maintenance of sewer pipes and renewal plans of many water utilities. This study explores the simultaneous utilization of physical and environmental features of sewer pipes in sewer structural condition prediction. Three (3) hybrid machine learning models which are the combination of Bagging (BG), Dagging (DG), and Rotation Forest (RotF) ensembles with a J48 Decision Tree (J48DT) based classifier were used to predict sewer pipe conditions in Ålesund city, Norway. The classification performance of the machine learning models was evaluated using the area under the receiver operating characteristic (AUC-ROC) and the area under the precision-recall (AUC-PRC) curves. The RotF-J48DT model had the highest (AUC-ROC = 0.857, AUC-PRC = 0.918) values, followed by the BG-J48DT, and the base classifier J48DT. The RotF-J48DT hybrid model should be considered when predicting the condition of sewer pipes in the study area.
ISSN:1573-062X
1744-9006
DOI:10.1080/1573062X.2023.2217430