Integration of Temperature, TDS, and Turbidity Sensors for Water Quality Monitoring and Machine Learning Application

Monitoring water quality is essential to guaranteeing the well-being and security of human populations as well as ecosystems. This study uses Internet of Things (IoT) devices, and sensors to investigate cutting-edge approaches and technologies for real-time water quality monitoring. It is possible to continuously detect important factors like temperature, TDS, and turbidity by establishing a network of smart sensors in different water bodies. For analysis, the gathered data is sent to a centralized system. In this study, the researchers use a database for future machine-learning applications. The study shows how combining machine learning and the Internet of Things may improve environmental monitoring and provide a scalable answer to address the growing challenges of water pollution and resource management.