Leak Detection in Water Distribution Networks via Pressure Analysis Using a Machine Learning Ensemble

Vivencio Fuentes; Jhoanna Pedrasa

Society with Future: Smart and Liveable Cities. First EAI International Conference, SC4Life 2019, Braga, Portugal, December 4-6, 2019, Proceedings

Research Article

Leak Detection in Water Distribution Networks via Pressure Analysis Using a Machine Learning Ensemble

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@INPROCEEDINGS{10.1007/978-3-030-45293-3_3,
    author={Vivencio Fuentes and Jhoanna Pedrasa},
    title={Leak Detection in Water Distribution Networks via Pressure Analysis Using a Machine Learning Ensemble},
    proceedings={Society with Future: Smart and Liveable Cities. First EAI International Conference, SC4Life 2019, Braga, Portugal, December 4-6, 2019, Proceedings},
    proceedings_a={SC4LIFE},
    year={2020},
    month={6},
    keywords={Water distribution networks Leak detection Machine learning},
    doi={10.1007/978-3-030-45293-3_3}
}

Vivencio Fuentes
Jhoanna Pedrasa
Year: 2020
Leak Detection in Water Distribution Networks via Pressure Analysis Using a Machine Learning Ensemble
SC4LIFE
Springer
DOI: 10.1007/978-3-030-45293-3_3

Vivencio Fuentes¹^,*, Jhoanna Pedrasa¹^,*

1: University of the Philippines, Diliman

*Contact email: vivencio.fuentes@eee.upd.edu.ph, jipedrasa@up.edu.ph

Abstract

Water distribution networks (WDNs) are vital infrastructure which serve as a means for public utilities to deliver potable water to consumers. Naturally, pipelines degrade over time, causing leakages and pipe bursts. Damaged pipelines allow water to leak through, incurring significant economic losses. Mitigating these losses are important, especially in areas with water scarcity, to allow consumers to have adequate water supply. Globally, as the population increases, there is a need to make water distribution efficient, due to the rising demand. Thus, leak detection is vital for reducing the system loss of the network and improving efficiency.

Keywords: Water distribution networks Leak detection Machine learning

Published: 2020-06-05
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-45293-3_3