A Model-Based Bayesian Framework for Pipeline Leakage Enumeration and Location Estimation

Juan Li (), Ying Wu (), Wenjun Zheng and Changgang Lu ()
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Juan Li: Jilin University
Ying Wu: Jilin University
Wenjun Zheng: Jilin University
Changgang Lu: Jilin University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 13, No 6, 4397 pages

Abstract: Abstract With the development of cities, the water resources loss and environmental pollution caused by pipeline leakage need to be solved urgently. In this paper, a probabilistic method of model-based Bayesian analysis is designed to solve the multi-leakage detection problem of reservoir pipeline valve system. Bayesian inference selects the model best suited to the measured data. This process estimates the number of leaks and then extracts the leak locations from a model that measures data preferences. In this paper, according to the characteristics of water head in pipeline, the Likelihood function of water head for Bayesian evidence calculation is given. It solves the problem that the location ability of recent research methods is limited by leakage location. The number and locations of leakages can be determined simultaneously. Different experimental Settings and scenarios are given to verify the effectiveness of the proposed method. For three leaks that do not contain tight leaks, the RMSE of each leak is 2.3068 m, and in the case of tight leaks, the average RMSE of each leak is 3.5011 m. The results demonstrating that this model-based Bayesian analysis is an accurate tool for leakage enumeration and location estimation.

Keywords: Leakage localization; Leakage enumeration; Bayesian inference; Nested sampling; Transient pressure wave (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11269-021-02955-8

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