Journal of Pioneering Artificial Intelligence Research

Abstract

This study investigates household water demand and supply dynamics in Ilorin Metropolis, Nigeria, positioning the findings within the framework of sustainable water management enabled by Artificial Intelligence (AI) and the Internet of Things (IOT). Data were collected from 225 households across ten locations using structured questionnaires, complemented with interviews from the Water Board. Results reveal that water access is constrained by socio-economic and infrastructural challenges, including gendered responsibility for water collection, low-to-moderate income levels, and reliance on limited water sources. Tap water accounts for 70.11% of supply; however, access remains inconsistent, requiring long collection distances and time investments. Most households adopt water storage strategies to cope with supply unreliability. Total household demand was estimated at 27,911.25 L/day, while supply remains insufficient due to pipe leakages, erratic power supply, and operational inefficiencies. A strong positive correlation (r = 0.9996) between demand and supply indicates significant potential for predictive analytics and system optimization. The study proposes the integration of IoT-enabled smart water meters and AI-driven predictive models for real-time monitoring, leakage detection, demand forecasting, and efficient water allocation. These technologies offer a pathway toward resilient, data-driven, and sustainable urban water systems in developing cities.