Leveraging Artificial Intelligence for Comprehensive Analysis of Community and Rural Aqueduct Systems - Salazar-Gámez, L.; Ojeda-Ocaña, R.; Rosero-Lombana, P.; Calpa-Villarreal, J.; Chañag-Checa, S.; Insuasty-Enriquez, J.; Cerón-Rosales, A.; Garzón, T. (Salehan Institute of Higher Education, 2026)
This interdisciplinary study presents a comprehensive evaluation of fourteen community-managed rural aqueduct systems in Pasto, Colombia, integrating technical, environmental, administrative-financial, and psychosocial dimensions. The research employs a mixed-methods approach, incorporating both structured and unstructured data. These data are analyzed through Exploratory Data Analysis, dimensionality reduction techniques, and generative artificial intelligence (AI) tools. The methodology employed is anchored in the framework of Integrated Water Resources Management (IWRM), a multifaceted approach to managing water resources. This framework facilitates a nuanced understanding of the sustainability challenges and management practices that characterize decentralized rural water supply systems. The findings of the study indicate that while technical variables are predominantly structured and quantifiable, psychosocial dimensions rely heavily on unstructured, qualitative data. Preliminary technical analysis indicated that while water sources generally exceed current demand, treatment coverage is limited, and none of the systems meet potable water standards. A thorough review of the environmental assessments yielded several key findings. First, there were notable deficiencies in source protection, planning, and regulatory compliance. Second, while there was some progress in administrative components, digital and labor formalization remained critical gaps. The psychosocial results indicated a high level of community commitment; however, they also revealed limited participation, weak leadership legitimacy, and persistent institutional distrust. AI-enhanced text mining and sentiment analysis facilitated the clustering of aqueducts into distinct management profiles, revealing divergent emphases between technical operations and administrative-social performance. Overall, the study demonstrates the value of AI-supported diagnostics for community water systems and recommends integrating participatory methodologies and adaptive public policies to foster equitable, resilient, and sustainable rural water governance.