A Context-Aware, Predictive Decision Support System for Water Quality and Infrastructure Management at Asa Dam, Nigeria

Authors: *Aderemi, I.A., Benson, K.P., Audu, M.F., Audu, I.B., Tijani, S.A. And Eleso, S.A.

DOI Info: http://doi.org/10.5281/zenodo.18060979

ABSTRACT

In rapidly urbanising regions such as Ilorin, Nigeria, water utilities face mounting pressures from ageing infrastructure, ecological degradation, and limited operational intelligence, compounded by the absence of real-time monitoring and predictive capabilities. This study develops a conceptual, context-aware Decision Support System (DSS) framework to strengthen the resilience and efficiency of the Asa Dam Water Treatment Plant, Ilorin. Guided by Design Science Research Methodology, the framework synthesises global advances in digital water management and the specific institutional and environmental realities of sub-Saharan Africa. The architecture integrates Internet of Things (IoT) sensors, Supervisory Control and Data Acquisition (SCADA) systems, Geographic Information Systems (GIS), and predictive algorithms, including Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks, to enable real-time water quality monitoring, anomaly detection, demand forecasting, and ecological risk zoning. Distinctively, the framework embeds explainable artificial intelligence (XAI), stakeholder engagement mechanisms, and hybrid cloud–edge deployment to ensure transparency, accountability, and adaptability in resource-constrained settings. Although empirical implementation is beyond the present scope, the study contributes a technically justified, ethically grounded, and locally adaptable blueprint that complements ongoing revitalisation efforts in Nigeria’s water sector. It further provides a transferable foundation for piloting and institutional integration in comparable utilities across sub-Saharan Africa, aligning with the objectives of Sustainable Development Goal 6.

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