Sinkhole Risk-Based Sensor Placement for Leakage Localization in Water Distribution Networks with a Data-Driven Approach

Gabriele Medio, Giada Varra, Çağrı Alperen İnan, Luca Cozzolino () and Renata Della Morte
Additional contact information
Gabriele Medio: Department of Engineering, University of Naples Parthenope, 80143 Naples, Italy
Giada Varra: Department of Engineering, University of Naples Parthenope, 80143 Naples, Italy
Çağrı Alperen İnan: Department of Engineering, University of Naples Parthenope, 80143 Naples, Italy
Luca Cozzolino: Department of Engineering, University of Naples Parthenope, 80143 Naples, Italy
Renata Della Morte: Department of Engineering, University of Naples Parthenope, 80143 Naples, Italy

Sustainability, 2024, vol. 16, issue 12, 1-20

Abstract: Leakages from damaged or deteriorated buried pipes in urban water distribution networks may cause significant socio-economic and environmental impacts, such as depletion of water resources and sinkhole events. Sinkholes are often caused by internal erosion and fluidization of the soil surrounding leaking pipes, with the formation of soil cavities that may eventually collapse. This in turn causes road disruption and building foundation damage, with possible victims. While the loss of precious water resources is a well-known problem, less attention has been paid to anthropogenic sinkhole events generated by leakages in water distribution systems. With a view to improving urban smart resilience and sustainability of urban areas, this study introduces an innovative framework to localize leakages based on a Machine learning model (for the training and evaluation of candidate sets of pressure sensors) and a Genetic algorithm (for the optimal sensor set positioning) with the goal of detecting and mitigating potential hydrogeological urban disruption due to water leakage in the most sensitive/critical locations. The application of the methodology on a synthetic case study from literature and a real-world case scenario shows that the methodology also contributes to reducing the depletion of water resources.

Keywords: machine learning; Genetic algorithm; water distribution network; pipe leakage localization; optimization; urban environment; human-induced sinkhole; urban ground collapse (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/12/5246/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/12/5246/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:12:p:5246-:d:1418790

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().