School of Engineering, The University of Tokyo　Research Center for Water Environment Technology

[Lecture abstract]
Water utilities aim to ensure a consistent and reliable water supply, with sufficient pressure and meeting quality standards. However, in today’s tightly interconnected world, urban water infrastructure cannot be understood in isolation. It must be considered in conjunction with its associated cyber-physical systems, such as sensors and smart metres, as well as other infrastructure systems and services. This seminar provides an overview of the essentials from machine learning and complexity science that can be used to model water distribution systems and support the decision-making process of water utilities.

[Lecturer and his research]
Dr. Herrera's research interests focus on using data analytics and network science to improve the resilience and asset management of infrastructure systems. He has extensive experience in water systems, where he has explored the use of predictive analytics for water demand forecasting, developed complex systems solutions for water network partitioning into district metered areas, and created network and graph machine learning-based tools for sensor placement and anomaly detection. Dr Herrera’s research also extends to other infrastructure systems, such as energy, telecommunications, and transport.