Intelligent optimization of rural water infrastructure through big data and predictive models: Insights from Latin America
DOI:
https://doi.org/10.51252/rcsi.v5i1.762Keywords:
Algorithms, Digitalization, Monitoring, Supply, SustainabilityAbstract
Access to potable water in rural areas continues to face structural challenges due to technological, operational, and planning gaps. In this study, we reviewed the state of the art on the use of Big Data and Artificial Intelligence for optimizing rural water infrastructure. We conducted a systematic review using the Scopus database, covering publications from 2015 to 2025. We identified 582 articles, of which 48 met the inclusion criteria. Our results show that predictive models and massive data analysis have improved operational efficiency by anticipating failures in distribution networks with up to 85% accuracy, thereby reducing losses. Additionally, technologies such as IoT sensors, digital twins, and automated systems have been successfully implemented in various countries, generating positive impacts on service sustainability. We conclude that the digitalization of potable water management, through AI and Big Data, constitutes an effective strategy to enhance the resilience and quality of supply in rural settings. These findings provide key inputs for designing policies and technological solutions applicable to regions such as San Martín, Peru.
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