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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472 EISSN: 1735-1472
Vol. 12, No. 8, 2015, pp. 2687-2696
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Bioline Code: st15252
Full paper language: English
Document type: Research Article
Document available free of charge
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International Journal of Environment Science and Technology, Vol. 12, No. 8, 2015, pp. 2687-2696
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The application of the cluster identification method for the detection of leakages in water distribution networks
Lin, H.-Y.; Lin, B.-W.; Li, P.-H. & Kao, J.-J.
Abstract
To reduce the amount of water wastage caused
by leakage, the utilities have to monitor and detect leakage
of water distribution networks periodically. In order to
identify leaking pipelines efficiently when limited resources
are available, a cluster identification method (CIM) is
proposed to establish a priority for leakage detection and to
assess whether spatial clusters of high failure-prone areas
exist. The proposed CIM evaluates the difference between
the observed data and simulated trials to determine the
statistical significance of each cluster; a method previously
applied only in epidemiology studies to assess the occurrence
probabilities of rare diseases for spatial clusters. The
CIM suggested in this study is the overlapping local case
proportions (OLCP) that uses grids to scope the entire area
and then to simulate the number of failures in the neighborhood
of each grid. The simulated failure ratios are then
compared with the existing records to determine the statistical
significance. The statistical significance represents
the potential of the grid requiring further leakage detection.
Three failure probability estimation methods, including
local average, global average, and empirical equation, are
utilized to analyze the suitability of the OLCP for use with
various probability inputs. A case study in the central
region of Taiwan was implemented to demonstrate the
applicability of the proposed method. The results indicate
that the rate of failure in the following year found within
the spatial clusters determined by the OLCP was twice the
average amount and thus provided valuable information
used to prioritize the pipelines for further inspection.
Keywords
Water leakage detection; Spatial clusters of high failure-prone areas; Overlapping local case proportions; Failure probability; Statistical significance
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