Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network Article Swipe
Lam Van Nguyen
,
Dieu Tien Bui
,
Razak Seidu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3222823
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3222823
Assessment of sewer condition is one of the critical steps in asset management and support investment decisions; therefore, condition assessment models with high accuracy are important that can help utility managers and other authorities correctly assess the current condition of the sewage network and effectively initiate maintenance and rehabilitation strategies. The main objective of this research is to assess the potential application of machine learning (ML) algorithms for predicting the condition of sewer pipes with a case study in Ålesund city, Norway. Nine physical factors (i.e., age, diameter, depth, slope, length, pipe type, material, pipe form, and connection type) and ten environmental factors (i.e., rainfall, geology, landslide area, building area, population, land cover, groundwater, traffic volume, distance to road, and soil type) were used to assess the sewer conditions employing seventeen ML models. After processing the sewer inspections, 1159 of 1449 individual pipelines were used to train the sewer condition model. The performance of ML models was validated using the 290 remaining inspected sewer pipes. The area under the Receiver Operating Characteristic (AUC-ROC) curve and accuracy (ACC) showed that the Random Forest (AUC-ROC = 77.6% and ACC = 78.3%) is a sensitive model for predicting the condition of sewer pipes in the study area. Based on the Random Forest model, maps of predicted conditions of sewers were generated that may be useful for utilities and water managers to establish future sewer system maintenance strategies.
Related Topics
Concepts
Sanitary sewer
Asset management
Environmental science
Civil engineering
Pipeline transport
Predictive modelling
Computer science
Investment (military)
Hydrology (agriculture)
Transport engineering
Engineering
Environmental engineering
Geotechnical engineering
Machine learning
Political science
Economics
Finance
Law
Politics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2022.3222823
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/09954004.pdf
- OA Status
- gold
- Cited By
- 18
- References
- 89
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312371890
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312371890Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2022.3222823Digital Object Identifier
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Comparison of Machine Learning Techniques for Condition Assessment of Sewer NetworkWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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Lam Van Nguyen, Dieu Tien Bui, Razak SeiduList of authors in order
- Landing page
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https://doi.org/10.1109/access.2022.3222823Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/09954004.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/09954004.pdfDirect OA link when available
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Sanitary sewer, Asset management, Environmental science, Civil engineering, Pipeline transport, Predictive modelling, Computer science, Investment (military), Hydrology (agriculture), Transport engineering, Engineering, Environmental engineering, Geotechnical engineering, Machine learning, Political science, Economics, Finance, Law, PoliticsTop concepts (fields/topics) attached by OpenAlex
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18Total citation count in OpenAlex
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2025: 9, 2024: 6, 2023: 3Per-year citation counts (last 5 years)
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89Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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