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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472
EISSN: 1735-2630
Vol. 12, No. 8, 2015, pp. 2687-2696
Bioline Code: st15252
Full paper language: English
Document type: Research Article
Document available free of charge

International Journal of Environment Science and Technology, Vol. 12, No. 8, 2015, pp. 2687-2696

 en 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.


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.

Water leakage detection; Spatial clusters of high failure-prone areas; Overlapping local case proportions; Failure probability; Statistical significance

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