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Leakage Rate Model of Urban Water Supply Networks Using Principal Component Regression Analysis

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Abstract

To analyze the factors affecting the leakage rate of water distribution system, we built a macroscopic “leakage rate–leakage factors” (LRLF) model. In this model, we consider the pipe attributes (quality, diameter, age), maintenance cost, valve replacement cost, and annual average pressure. Based on variable selection and principal component analysis results, we extracted three main principle components—the pipe attribute principal component (PAPC), operation management principal component, and water pressure principal component. Of these, we found PAPC to have the most influence. Using principal component regression, we established an LRLF model with no detectable serial correlations. The adjusted R 2 and RMSE values of the model were 0.717 and 2.067, respectively. This model represents a potentially useful tool for controlling leakage rate from the macroscopic viewpoint.

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Acknowledgements

This study was supported by the Ministry of Science and Technology of China (No. 2014ZX07203-009), the Fundamental Research Funds for the Central Universities, and the Program for New Century Excellent Talents at the University of China.

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Authors and Affiliations

  1. School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China

    Zhiguang Niu & Chong Wang

  2. Key Laboratory of Pollution Processes and Environmental Criteria of Ministry of Education, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China

    Ying Zhang

  3. Binhai Industrial Technology Research Institute of Zhejiang University, Tianjin, 300450, China

    Xiaoting Wei

  4. Tianjin Urban Construction Design Institute, Tianjin, 300122, China

    Xili Gao

Authors
  1. Zhiguang Niu
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  2. Chong Wang
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  3. Ying Zhang
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  4. Xiaoting Wei
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  5. Xili Gao
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Correspondence to Ying Zhang.

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Niu, Z., Wang, C., Zhang, Y. et al. Leakage Rate Model of Urban Water Supply Networks Using Principal Component Regression Analysis. Trans. Tianjin Univ. 24, 172–181 (2018). https://doi.org/10.1007/s12209-017-0090-x

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  • DOI: https://doi.org/10.1007/s12209-017-0090-x

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