Spatial heterogeneity assessment of factors affecting sewer pipe blockages and predictions. (15th April 2021)
- Record Type:
- Journal Article
- Title:
- Spatial heterogeneity assessment of factors affecting sewer pipe blockages and predictions. (15th April 2021)
- Main Title:
- Spatial heterogeneity assessment of factors affecting sewer pipe blockages and predictions
- Authors:
- Okwori, E.
Viklander, M.
Hedström, A. - Abstract:
- Highlights: Prevalent blockage influencers in sewer networks were delineated. The degree of influence serves as a blockage maintenance prioritisation parameter. Increased clustering of blockages may be connected to increased maintenance need. Spatial variability of blockages may be linked to improved predictability. Abstract: Efficient management of sewer blockages requires increased preventive maintenance planning. Conventional approaches to the management of blockages in sewer pipe networks constitute largely unplanned maintenance stemming from a lack of adequate information and diagnosis of blockage causative mechanisms. This study mainly investigated a spatial statistical approach to determine the influence of explanatory factors on increased blockage propensity in sewers based on spatial heterogeneity. The approach consisted of the network K-function analysis, which provided an understanding of the significance of the spatial variation of blockages. A geographically-weighted Poisson regression then showed the degree of influence that explanatory factors had on increased blockage propensity in differentiated segments of the sewer pipe network. Lastly, blockage recurrence predictions were carried out with Random Forest ensembles. This approach was applied to three municipalities. Explanatory factors such as material type, number of service connections, self-cleaning velocity, sagging pipes, root intrusion risk, closed-circuit television inspection grade and distance toHighlights: Prevalent blockage influencers in sewer networks were delineated. The degree of influence serves as a blockage maintenance prioritisation parameter. Increased clustering of blockages may be connected to increased maintenance need. Spatial variability of blockages may be linked to improved predictability. Abstract: Efficient management of sewer blockages requires increased preventive maintenance planning. Conventional approaches to the management of blockages in sewer pipe networks constitute largely unplanned maintenance stemming from a lack of adequate information and diagnosis of blockage causative mechanisms. This study mainly investigated a spatial statistical approach to determine the influence of explanatory factors on increased blockage propensity in sewers based on spatial heterogeneity. The approach consisted of the network K-function analysis, which provided an understanding of the significance of the spatial variation of blockages. A geographically-weighted Poisson regression then showed the degree of influence that explanatory factors had on increased blockage propensity in differentiated segments of the sewer pipe network. Lastly, blockage recurrence predictions were carried out with Random Forest ensembles. This approach was applied to three municipalities. Explanatory factors such as material type, number of service connections, self-cleaning velocity, sagging pipes, root intrusion risk, closed-circuit television inspection grade and distance to restaurants showed significant spatial heterogeneity and varying impacts on blockage propensity. The Random Forest ensemble predicted blockage recurrence with 60–80% accuracy for data from two municipalities and below 50% for the last. This approach provides knowledge that supports proactive maintenance planning in the management of blockages in sewer pipe networks. Graphical abstract: Image, graphical abstract … (more)
- Is Part Of:
- Water research. Volume 194(2021)
- Journal:
- Water research
- Issue:
- Volume 194(2021)
- Issue Display:
- Volume 194, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 194
- Issue:
- 2021
- Issue Sort Value:
- 2021-0194-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-04-15
- Subjects:
- Network k-function -- Geographically-weighted poisson regression -- Random forest ensembles -- Maintenance prioritisation -- Proactive maintenance
Water -- Pollution -- Research -- Periodicals
363.7394 - Journal URLs:
- http://catalog.hathitrust.org/api/volumes/oclc/1769499.html ↗
http://www.sciencedirect.com/science/journal/00431354 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.watres.2021.116934 ↗
- Languages:
- English
- ISSNs:
- 0043-1354
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9273.400000
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British Library HMNTS - ELD Digital store - Ingest File:
- 22048.xml