Low-Cost Water Quality Monitoring in Rural Areas Based on Artificial Intelligence and the Internet of Things

This study proposes a low-cost water quality monitoring system based on Metal-Organic Framework (MOF) materials, integrated with Internet of Things (IoT) technology and Artificial Intelligence (AI) algorithms, for real-time and accurate monitoring of water quality in rural areas. The system was tested in several rural regions, collecting data on water quality parameters such as pH, dissolved oxygen, and turbidity to validate its effectiveness. The MOF-based sensors demonstrated high sensitivity and accuracy, closely matching traditional chemical analysis methods, with good stability in detecting low-concentration pollutants. NB-IoT technology was used for data transmission, ensuring stable communication even in complex geographical conditions to meet real-time monitoring needs. AI algorithms, including Support Vector Machines, decision trees, and neural networks, analyzed water quality data in real-time, accurately identifying anomalies with an alert accuracy rate exceeding 90%. Compared to traditional methods, this system significantly reduces costs, enhances monitoring efficiency, and improves real-time capabilities, making it especially suitable for rural areas with weak infrastructure. The results demonstrate significant advantages in accuracy, efficiency, and cost control, with broad application potential. application prospects.