AQUATWIN: A Digital Twin Framework for Early Detection of Water Contamination

The stability and well-being of human societies and communities depend on having access to clean, safe water. The sustainability of our civilization rests on our ability to guarantee that the water bodies we utilize are free of harmful contaminants. Digital twins are emerging as a powerful tool for managing complex systems in a variety of Internet of Things applications, including water quality monitoring. In this paper, we discuss the development and implementation of a digital twin for water quality monitoring that mimics sensory data integrated with a structural model of the water quality system to create a multipurpose simulation of water quality conditions. The model can be used to explore changes in water quality based on various scenarios. It employs establishing effective communication and coordination between computational processes, facilitating the utilization of waste inputs for conducting data analysis through machine learning techniques.