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In Colombia, an average of 41% of potable water is lost in water distribution systems, including both technical and non-technical losses. In order to reduce these losses, improving operational management is of paramount importance. This paper presents a methodology that integrates the technical expertise of operational engineers with data science to improve the management, monitoring, and maintenance of water distribution networks, focusing on efficiency and sustainability. The research introduces techniques for automated error detection and correction in large volumes of operational data, using linear regression models and deep neural networks. These models allow for the correction of missing or erroneous data in the flow measurement system, improving the precision in the assessment of the infrastructure’s condition and fostering more informed and strategic decisions. The collaborative approach between computational capabilities and expert judgment results in useful models that contribute to the optimal management of the water service and the utilization of modern technological tools.

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Improvement in the Management of Potable Water Distribution Using Data Science for the Detection and Correction of Errors in Operational Measurement Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Rafael Rincon Villamizar,
  • J. L. Villa

摘要

In Colombia, an average of 41% of potable water is lost in water distribution systems, including both technical and non-technical losses. In order to reduce these losses, improving operational management is of paramount importance. This paper presents a methodology that integrates the technical expertise of operational engineers with data science to improve the management, monitoring, and maintenance of water distribution networks, focusing on efficiency and sustainability. The research introduces techniques for automated error detection and correction in large volumes of operational data, using linear regression models and deep neural networks. These models allow for the correction of missing or erroneous data in the flow measurement system, improving the precision in the assessment of the infrastructure’s condition and fostering more informed and strategic decisions. The collaborative approach between computational capabilities and expert judgment results in useful models that contribute to the optimal management of the water service and the utilization of modern technological tools.