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Survival Function in the Analysis of the Factors Influencing the Reliability of Water Wells Operation

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  • Kozłowski Edward

    (Lublin University of Technology)

  • Kowalska Beata

    (Lublin University of Technology)

  • Kowalski Dariusz

    (Lublin University of Technology)

  • Mazurkiewicz Dariusz

    (Lublin University of Technology)

Abstract

In common studies on the groundwater intake reliability, suitable methods of statistical interference are usually employed in order to use the analysis and modelling results in relation to the entire population. Exponential distribution of random variables and events, Weibull distribution, normal, log-normal distribution as well as Poisson distribution are used most frequently. The distribution type of failure duration is identified on the basis of the data collected from a random investigated sample. The data collected for this purpose, apart from the object identification, usually pertain to the information on damages, service activities and intervals in operation. However, in some cases, additional data is required, because the reliability of water intakes is also influenced by the quality and quantity of water in the source. This is why, this paper will present an analysis of reliability data from a water supply sources consisting of deep wells taking into consideration additional, potential failure reasons. The aim of the study is to provide a tool of comparison of deep wells reliability, considering that the biggest differences between survival functions is a measure of reliability between objects.

Suggested Citation

  • Kozłowski Edward & Kowalska Beata & Kowalski Dariusz & Mazurkiewicz Dariusz, 2019. "Survival Function in the Analysis of the Factors Influencing the Reliability of Water Wells Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4909-4921, November.
  • Handle: RePEc:spr:waterr:v:33:y:2019:i:14:d:10.1007_s11269-019-02419-0
    DOI: 10.1007/s11269-019-02419-0
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    References listed on IDEAS

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    1. Kabir, Golam & Tesfamariam, Solomon & Sadiq, Rehan, 2015. "Predicting water main failures using Bayesian model averaging and survival modelling approach," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 498-514.
    2. Jiang, R. & Jardine, A.K.S., 2008. "Health state evaluation of an item: A general framework and graphical representation," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 89-99.
    3. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    4. Debón, A. & Carrión, A. & Cabrera, E. & Solano, H., 2010. "Comparing risk of failure models in water supply networks using ROC curves," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 43-48.
    5. Yamijala, Shridhar & Guikema, Seth D. & Brumbelow, Kelly, 2009. "Statistical models for the analysis of water distribution system pipe break data," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 282-293.
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