Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities

Digital twins provide insights into physical objects by serving as advanced virtual representations. Their sensors capture detailed information about an object’s functionality through their use of various sensors. It is possible to gain a deep understanding of the object’s performance and potential areas for improvement by collecting data, which includes metrics such as energy output, temperature, and weather conditions. Digital twins are becoming important in a variety of research and industrial application sectors as production lines and processes become more digitalized and as improved data analysis techniques such as machine learning and enhanced visualization techniques are used. There is no unified definition of the digital twin concept in scientific literature, which results in imprecise applications and the weakening of its terminology. However, this study demonstrates how digital twin models can be applied to urban drainage systems. As a result, this highlights the relatively novel use of digital twins within the field of urban water system engineering. Our review of the language, practices, and directions in smart stormwater management provides a framework to organize and comprehend the current research landscape while highlighting crucial areas for future research. Our results demonstrate that there is near-unanimous agreement within the literature that smart technology has been, or will be, advantageous for stormwater management. However, while some progress has been made in terms of quantity management, maturity in water quality management has not yet been achieved. This study examines the scientific literature on digital twins in the application of artificial intelligence for smart city stormwater infrastructure systems, specifically focusing on urban drainage systems. A demonstration of the workflow and features of current digital twin applications in urban drainage systems is also presented, providing valuable insights and guidance for future research and development in this field.