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Cluster Analysis and Predictive Modeling of Urban Water Distribution System Leaks with Socioeconomic and Engineering Factors

Author

Listed:
  • Qing Shuang

    (Beijing Jiaotong University)

  • Rui Ting Zhao

    (Beijing Jiaotong University)

  • Erik Porse

    (California State University
    University of California, Los Angeles)

Abstract

Water distribution network (WDN) failures can disrupt operations and cause economic damage. Although leakage has been widely discussed, few studies have integrated spatial clusters with engineering, environmental, and socioeconomic factors simultaneously. This study proposes an approach to explore the role of socioeconomic factors in understanding leak risks. Using a unique data set of more than 4,000 reported leak events within the City of Los Angeles (2010–2013), the analysis (1) assesses the effectiveness of including socioeconomic factors with engineering factors in explaining observed leaks, (2) identifies spatial clusters of leaks, and (3) develops a predictive model with machine learning to identify spatial areas with high risks of failure. Results indicate that distinct clusters of leaks are evident, accounting for 20–30% of all leaks in the study area in a given year. Multivariate regression modeling showed that geography, socioeconomic, and engineering factors are statistically significant in predicting leaks. A predictive model with machine learning was developed, identifying key factors. The model had accuracy rates of 93.29% and 92.45% for interpolation and extrapolation prediction scenarios, respectively. The approach demonstrates the potential value of incorporating socioeconomic indicators into the models for WDN rehabilitation. Moreover, the approach demonstrates how municipal leak loss mitigation programs can consider a broad set of predictive factors to optimize investments.

Suggested Citation

  • Qing Shuang & Rui Ting Zhao & Erik Porse, 2024. "Cluster Analysis and Predictive Modeling of Urban Water Distribution System Leaks with Socioeconomic and Engineering Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 385-400, January.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:1:d:10.1007_s11269-023-03676-w
    DOI: 10.1007/s11269-023-03676-w
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    References listed on IDEAS

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    1. Kiyoumars Roushangar & Roghayeh Ghasempour & Vahid Nourani, 2022. "Spatiotemporal Analysis of Droughts Over Different Climate Regions Using Hybrid Clustering Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 473-488, January.
    2. Ana Paula Pereira Silveira & Herlander Mata-Lima, 2021. "Assessing Energy Efficiency in Water Utilities Using Long-term Data Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2763-2779, July.
    3. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Kiyoumars Roushangar & Roghayeh Ghasempour & Vahid Nourani, 2022. "Correction to: Spatiotemporal Analysis of Droughts Over Different Climate Regions Using Hybrid Clustering Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 489-489, January.
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