Development of a sensor-based and neural network-driven water quality monitoring system: an automated approach

Abstract Automated water quality monitoring systems increasingly combine low-cost sensors with artificial intelligence to improve accessibility and real-time analysis. However, many existing solutions rely on expensive instrumentation, pre-trained models, or large structured datasets, limiting their applicability in underserved regions. This project presents an end-to-end, low-cost monitoring platform tailored for resource-constrained environments. The system integrates custom-designed sensors that measure water parameters such as pH, turbidity, conductivity, temperature, and color, feeding real-time data into a locally trained artificial neural network (ANN). A total of 92 water samples were collected and labeled by expert evaluation into three classes: potable, non-potable, and regular. The ANN achieved 91% overall accuracy, with high precision and recall for potable and non-potable classes, and moderate performance for regular water. The system is deployed through a web interface that allows real-time data visualization, supporting continuous dataset expansion and future model retraining. This work demonstrates a novel and scalable approach for deploying IoT and AI-based water diagnostics in settings with limited infrastructure.