On June 28, 2024, via its IAHR/IWA Joint Committee on Outfall System, IAHR organized the Webinar on Modeling Effluent Mixing and Transport Using Machine Learning Methods. Moderated by Prof. Majid Mohammadian, chair of this joint committee, the webinar featured a keynote presentation by Dr. Xiaohui Yan, associate professor of the Dalian University of Technology, China, providing a detailed exploration of the application of machine learning in the modeling of effluent mixing and transport.

Prof. Majid Mohammadian introduced the webinar and the joint committee, welcomed the participants and introduced the speaker, Dr. Xiaohui Yan, emphasizing Dr. Yan’s expertise and contributions to the field.

Dr. Xiaohui Yan delivered an in-depth keynote speech titled “Modeling Effluent Mixing and Transport Using Machine Learning Methods.” His presentation focused on the challenges posed by the three-dimensional variability and complex mechanisms involved in effluent mixing and transport processes. Dr. Yan contrasted traditional numerical models based on 3D computational fluid dynamics (CFD) techniques with machine learning (ML) approaches, noting the high computational costs and expertise required for CFD models. He provided an overview of recent advancements in machine learning techniques and their applications in water-related problems, explaining how ML can serve as a powerful tool for modeling complex physical processes. Detailed technical aspects of applying ML, including data collection, model training, and validation processes, were covered, emphasizing the ability of ML models to deliver accurate predictions with lower computational demands compared to traditional CFD models. The presentation also included several case studies demonstrating the practical application of ML in predicting effluent mixing and transport, showcasing successful implementations and illustrating their effectiveness.

The webinar concluded with a Q&A session where Dr. Xiaohui Yan addressed several pertinent questions from the attendees. Key topics covered included data requirements for ML models, comparative accuracy of ML and CFD models, practical implementation challenges, and future prospects of ML in water resources management. Dr. Yan provided insights into best practices for data collection and preprocessing, discussed validation metrics, shared experiences on overcoming practical challenges, and highlighted ongoing research efforts and potential collaborative projects.

The webinar successfully showcased the transformative potential of machine learning in modeling effluent mixing and transport. Dr. Xiaohui Yan’s detailed presentation, supported by practical examples and case studies, provided valuable insights into the application of ML techniques. The interactive Q&A session further enriched the discussion, addressing critical questions and exploring future directions.