AI and RF-Based Water Quality Monitoring Systems for Rural and Urban Nigeria

This paper investigates the use of Artificial Intelligence (AI) and Radio Frequency (RF)-based sensor systems to monitor water quality in rural and urban Nigeria. Nigeria suffers serious water quality issues as a result of insufficient facilities, pollution, and limited real-time monitoring capacity. Conventional water monitoring methods are frequently manual, time-consuming, and ineffective. Recent advances in RF sensor networks and AI-driven analytics present intriguing options for continuous, real-time water quality monitoring and prediction. This paper examines the existing technologies, communication protocols, sensor types, and AI models used in water monitoring systems. It assesses their performance, applicability, and limitations in the Nigerian setting. The combination of RF sensing technology with machine learning models improves anomaly identification, pollutant tracking, and decision-making processes. Obstacles such as infrastructural gaps, expensive deployment costs, and a lack of technical competence impede large-scale implementation. The paper highlights research gaps and makes recommendations for the scalable and sustainable implementation of smart water surveillance systems in Nigeria.