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The application of the cluster identification method for the detection of leakages in water distribution networks

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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.

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References

  • Alvisi S, Franchini M (2005) Rehabilitation scheduling of water distribution systems based on multi-objective genetic algorithms. In: Paper presented at the proceedings of the eight international conference on computing and control for the water industry, University of Exeter, UK

  • Best NG, Ickstadt K, Wolpert RL, Briggs DJ (2001) Chap 22 combining models of health and exposure data: the SAVIAH study. In: Elliott P, Wakefield J, Best N, Briggs D (eds) Spatial epidemiology: methods and applications. Oxford University Press, NYC

    Google Scholar 

  • Charalambous B (2005) Experiences in DMA redesign at the Water Board of Lemesos, Cyprus. In: Paper presented at the leakage 2005 conference proceedings

  • Chuang L-W (2008) District metering area partition procedure and optimization model for water distribution network leakage detection. National Chiao-Tung University, Taiwan

    Google Scholar 

  • Dandy GC, Engelhardt MO (2006) Multi-objective trade-offs between cost and reliability in the replacement of water mains. J Water Resour Plan Manag 132(2):79–88

    Article  Google Scholar 

  • Department of Geography, The University of Iowa (2007) DMAP IV. http://www.uiowa.edu/~gishlth/DMAP4

  • Farley M, Trow S (2003) Losses in water distribution networks: a practitioners’ guide to assessment, monitoring and control. International water association

  • Kleiner Y, Adams BJ, Rogers JS (1998) Long-term planning methodology for water distribution system rehabilitation. Water Resour Res 34(8):2039–2051

    Article  CAS  Google Scholar 

  • Kulldorff M, Nagarwalla N (1995) Spatial disease clusters: detection and inference. Stat Med 14:799–810

    Article  CAS  Google Scholar 

  • Mondéjar-Jiménez J, Vargas-Vargas M, Segarra-Oña M, Peiró-Signes A (2013) Categorizing variables affecting the proactive environmental orientation of firms. Int J Environ Res 7(2):495–500

    Google Scholar 

  • Openshaw S, Charlton M, Craft AW, Birch J (1988) Investigation of leukaemia clusters by use of a geographical analysis machine. The Lancet 331(8580):272–273

    Article  Google Scholar 

  • Rushton G, Lolonis P (1996) Exploratory spatial analysis of birth defect rates in an urban population. Stat Med 15:717–726

    Article  CAS  Google Scholar 

  • Smith GH (2001) Disease cluster detection methods: the impact of choice of shape on the power of statistical tests. http://www.cobblestoneconcepts.com/ucgis2summer/smith/SMITH.HTM.2013

  • Su YC, Mays LW, Duan N, Lansey KE (1987) Reliability based optimization model for water distribution systems. J Hydraul Eng (ASCE) 114(12):1539–1556

    Article  Google Scholar 

  • Walski TM, Pelliccia A (1982) Economic analysis of water main breaks. J Am Water Works Assoc 74(3):140–147

  • Yiannakoulias N (2009) Using population attributable risk to understand geographic disease clusters. Health Place 15(4):1142–1148. doi:10.1016/j.healthplace.2009.07.001

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The writers would like to thank the National Science Council of Taiwan, the Republic of China, for financially supporting this research under Contract No. NSC 101-2211-E-324-012. The writers would also like to express special thanks to Dr. Qiang Cai for his developing DMAP IV and assistance in helping the authors use the program in this study.

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Authors and Affiliations

  1. Department of Environmental Engineering and Management, Chaoyang University of Technology, 168, Jifong E. Rd., Wufong District, Taichung, 41349, Taichung County, Taiwan, ROC

    H.-Y. Lin & B.-W. Lin

  2. Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan, ROC

    P.-H. Li

  3. Institute of Environmental Engineering, National Chiao Tung University, Hsinchu, Taiwan, ROC

    J.-J. Kao

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  1. H.-Y. Lin
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  2. B.-W. Lin
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Correspondence to H.-Y. Lin.

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Lin, HY., Lin, BW., Li, PH. et al. The application of the cluster identification method for the detection of leakages in water distribution networks. Int. J. Environ. Sci. Technol. 12, 2687–2696 (2015). https://doi.org/10.1007/s13762-014-0666-0

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  • DOI: https://doi.org/10.1007/s13762-014-0666-0

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