Dynamic Water Quality Monitoring via IoT Sensor Networks and Machine Learning Technique

The development of sophisticated monitoring systems that can do thorough and real-time assessments has been spurred by growing worries about the quality of water. In this study, we suggest a unique method for dynamically monitoring the quality of water by combining machine learning techniques with an Internet of Things (IoT) sensor network. With carefully placed IoT sensors inside water bodies or distribution networks, the system is intended to continually gather multiple parameter data, such as pH, turbidity, temperature, and dissolved oxygen. Modern machine learning algorithms housed on cloud infrastructure are used to process and analyze the gathered data. Our method seeks to identify abnormalities, forecast changes in water quality, and offer current information on the state of water resources. Machine learning models are trained on past data in order to detect trends, spot departures from the norm, and make it easier to make proactive decisions in reaction to changes or possible pollutants. We outline the design of our Internet of Things (IoT) sensor network, how cloud computing is integrated for data processing, and how machine learning algorithms are put into practice for predictive analytics. We also go over the system's flexibility to changing environmental circumstances, scalability, and possible uses in environmental protection and water resource management.