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

This chapter explores the statistical modelling of digital capture data of water assets, focusing on principal components and cluster analyses of water tanks in Brazilian municipalities. The study is motivated by the need to enhance operational efficiency and service sustainability in water supply services, which manage extensive infrastructure assets. The research applies advanced statistical modelling methods to a sample of data from a Brazilian water utility, collected through innovative digital reality capture technologies. The methodology involves principal component analysis (PCA) and cluster analysis to characterize and group assets based on their quantitative and qualitative attributes. The study highlights the potential of digitalization to increase the reliability and quality of asset information, addressing challenges such as urban growth, natural resource limitations, and climate change. The results demonstrate the effectiveness of digitalization processes in improving asset management practices, offering opportunities for prioritizing resource allocation and making more informed decisions. The chapter concludes by emphasizing the benefits of investing in advanced data collection technologies and suggests future research directions, including the expansion of data analysis to other infrastructure assets and the exploration of AI algorithms for enhanced decision-making and risk management.