Machine Learning–Enabled IoT System for Portable and Real-Time Water Quality Monitoring
摘要
Humans require the ability to obtain clean water for survival. Illnesses are frequently caused by pathogenic microbes in drinking water, resulting in a pressing need for accessible and affordable testing procedures. Rural and agricultural regions, as well as the global community, face significant challenges in ensuring clean and safe drinking water, which is essential for maintaining public health. The development of a compact and portable water quality tester is presented in this research, which provides instant and accurate measurements of key water quality parameters including Total Dissolved Solids (TDS), temperature (in both Celsius and Fahrenheit), and parts per million (ppm). In different environments, from farms to urban areas, the device is adaptable due to its ease of operation, affordability, and efficiency. This tester enables individuals and communities to quickly assess water quality, thereby ensuring access to safe water and fostering better health standards. The integration of Internet of Things (IoT) technologies enables continuous data logging and real-time monitoring, allowing for the tracking of water quality trends over time. Beneficial this feature is for larger-scale applications, such as environmental monitoring and government water safety programs. This device delivers reliable and consistent results through the use of advanced sensor technologies and data processing techniques in its research. A water quality tester that significantly enhances the ability to detect contamination in water sources will contribute to sustainable water management and improved public health outcome.