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.
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Data Availability
The data is available in an online repository: https://github.com/erikporse/artes. Data used in the study was compiled from multiple sources, including: (CEC 2021); (County’s Enterprise GIS (eGIS) Steering Committee 2018); (Krishnakumar and Poston 2016); (OEHHA 2022); (Pincetl and LA Energy Atlas Development Team 2023); (Poston and De Groot 2014); (Poston and Stevens 2015).
Code Availability
Code generated or used during the study are available in an online repository: https://github.com/erikporse/artes.
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Acknowledgements
Many thanks to Eric Fournier, Dan Cheng, Claire Hirashiki, and Stephanie Pincetl at the California Center for Sustainable Communities within UCLA’s Institute of the Environment and Sustainability for guidance and technical assistance.
Funding
Support for this research was provided by the Beijing Social Sciences Foundation (grant number 18GLC070), the Fundamental Research Funds for the Central Universities (grant number 2019JBW007), and China Scholarship Council (grant number 201707095080). The Office of Water Programs at California State University, Sacramento also supported the research.Beijing Social Sciences Foundation,18GLC070,Qing Shuang,Fundamental Research Funds for the Central Universities,2019JBW007,Qing Shuang,China Scholarship Council,201707095080,Qing Shuang.
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Shuang Q. and Porse E. designed the research, collected the data, and programed the code; Zhao R.T. tested the model; Shuang Q. and Zhao R.T. wrote the original draft preparation; Porse E. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.
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Shuang, Q., Zhao, R.T. & Porse, E. Cluster Analysis and Predictive Modeling of Urban Water Distribution System Leaks with Socioeconomic and Engineering Factors. Water Resour Manage 38, 385–400 (2024). https://doi.org/10.1007/s11269-023-03676-w
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DOI: https://doi.org/10.1007/s11269-023-03676-w