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Access to clean drinking water remains a global challenge, impacting 2 billion people. Additionally, the water distribution sector experiences substantial financial losses, estimated at $14 billion annually due to high water loss. The digital transformation heralded by Industry 4.0 promises significant enhancements in water service efficiency. The Digital Water program launched by IWA in 2022 highlights the several benefits of digitalization in operational efficiency and water resource management. This study investigates the application of digital reality capture technologies to improve asset information for water supply services. Using statistical modelling techniques of principal components and cluster analyses, this research analyzes a database comprising 2,584 physical assets from water tanks in Brazil. The data analysis, conducted using IBM SPSS and visualized in PowerBI, demonstrates the potential of these technologies as a robust decision-making support tool for the Asset Management System (AMS).

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Statistical Modelling of Digital Capture Data of Water Assets: Principal Components and Cluster Analyses of Water Tanks in Brazilian Municipalities

  • 章节

  • 发表日期：

    2026年

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  • Wagner Oliveira de Carvalho,
  • Nuno Marques de Almeida,
  • Rui Cunha Marques,
  • Marta Castilho Gomes

摘要

Access to clean drinking water remains a global challenge, impacting 2 billion people. Additionally, the water distribution sector experiences substantial financial losses, estimated at $14 billion annually due to high water loss. The digital transformation heralded by Industry 4.0 promises significant enhancements in water service efficiency. The Digital Water program launched by IWA in 2022 highlights the several benefits of digitalization in operational efficiency and water resource management. This study investigates the application of digital reality capture technologies to improve asset information for water supply services. Using statistical modelling techniques of principal components and cluster analyses, this research analyzes a database comprising 2,584 physical assets from water tanks in Brazil. The data analysis, conducted using IBM SPSS and visualized in PowerBI, demonstrates the potential of these technologies as a robust decision-making support tool for the Asset Management System (AMS).