Course content

The course provides an overview of the main applications of hydroinformatics and water science data for all aspects of the urban water system. Focusing on state of art in systems analysis, simulation and optimization techniques, with an emphasis on water management problems, at both urban and catchment scales. The course provides an overview of current state of art in use of big data and urban water systems, focusing on challenges and opportunities. It then focuses on providing a good understanding of key data driven, artificial intelligence techniques such as neural networks, surrogate models and data mining that form the "brains" of water applications.

The course also provides the basis for understanding and managing the challenges of use of data sources and , decision making and quantification of uncertainty using probabilistic optimization techniques such as evolutionary optimization, genetic algorithms and hybrid approaches. Last but not least, the course provides an overview of the field from the other side: that of novel sources of data for urban water systems, including novel sensors and IoT applications for main elements of the system.

Learning outcome

Knowledge and competence

The topic shall give the students in-depth understanding of the state of art, challenges and opportunities of smart urban water systems. They shall understand the principles of data driven methods and evolutionary optimization that underpin most of the "intelligence" behind smart urban water systems as well as the basics of the opportunities and challenges afforded by new ubiquitous sensing and remote-control technologies.

Skills

The students shall be able to use data driven approaches and probabilistic optimization tools to solve relevant problems in water management in the context of smart urban water systems.

General competence

Students will develop a systems viewpoint to smart urban water and its link to emerging AI technologies and enhance their ability to critically evaluate problems and solutions in the context of smart water systems.

Learning methods and activities

The course will be organised in regular lectures and exercise hours where you will get assistance and guidance with the two assigned projects. A final oral exam will consist of a short discussion of the projects and open form questions.

Recommended previous knowledge

Some programming skills in f.ex Python is useful but not a requirement.

Required previous knowledge

TBM4265 or equivalent

Course materials

Notes of lectures, selection of journal articles.