Reality capture and statistical modelling for digital water asset management: case study of hydraulic infrastructure in Brazil
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Abstract
The provision of water supply services continues to be a significant global challenge, with approximately 2 billion people lacking access to safe drinking water (UNICEF/WHO, 2023). Additionally, the water distribution sector faces substantial financial losses, estimated at $14 billion annually due to high levels of water loss in the distribution system (World Bank Group, 2022). In developing countries, the average water loss is 35%, reaching up to 70% in some regions, while in developed countries, this average is 15%.
The digital transformation promoted by Industry 4.0 has the potential to increase the efficiency of water service providers. The Digital Water program, launched by the International Water Association (IWA, 2022), exemplifies how digitalization can improve operational efficiency and water resource management. Asset management in the water supply sector needs to advance, particularly in the quality of the informational base, often characterized by low-reliability data.
The study presented in this paper analyzes the methodology of asset reality capture (Niles et al., 2022) and the data base of a water utility in Brazil. Uni and bivariate statistical models were used at the IBM SPSS software (Cronk, 2018) to analyze 2,584 assets belonging to 106 reservoirs (entities) municipalities, characterized in 2 context variables and 26 metric variables.
In the application of statistical modeling of data confirmed the informational insufficiency of physical assets of the water sector, and to present the descriptive measures of the quantity and condition of the assets in the plant by means of graphical representations, including histograms and bar graphs.
The results of bivariate analyses generated relevant insights for water asset management by demonstrating significant correlations between some variables derived from the categories, attributes, condition and context of the physical assets. As an example, the identification of the concentration of some deficient reservoirs in regions of Rio de Janeiro (high population density and high MHDI) is highlighted and this indication will guide the decision of the company to prioritize investments in these assets.
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