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SoSe 2022 - WiSe 2022/23


Data Science and Artificial Intelligence for Urban Water Management


Cominola, Andrea




Fakultät V

Institut für Strömungsmechanik und Technische Akustik

35311100 FG Fluidsystemdynamik-Strömungstechnik der Maschinen und Anlagen

Physikalische Ingenieurwissenschaft



Cominola, Andrea


Learning Outcomes

During this course, the students will acquire first basic knowledge, and then more advanced notions about modelling, Data Science (DS) & Artificial Intelligence (AI) techniques for modelling and managing urban water systems, with theory, methods, and applications. They will learn the basics of mathematical modelling and simulation (model building, model calibration, model performance assessment), along with more sophisticated DS and AI techniques to model, simulate, and control urban water distribution networks. They will learn what the current research challenges in the field of urban water systems management are, with a focus on the latest DS and AI technologies. They will learn how to build a model of a water distribution network in a virtual environment. They will approach the practical implementation of solutions to currently relevant problems in modelling and operation of urban water distribution systems, with guided practical activities. They will learn how to read scientific literature. They will learn how to concisely analyze and present a research work.


The digital transition of urban water networks towards more data-driven and intelligent systems represents a primary opportunity to tackle the challenges posed by increasing population, urbanisation, and changing climate conditions. As the data-driven transformation reaches into the economy and society, ever-increasing amounts of data are generated by machines or processes based on emerging technologies, such as the Internet of Things (IoT), connected systems, and advanced modelling. While digital disruption has already transformed a number of other industries globally, the water sector has only recently embraced the digital transformation. This is the key to developing suitable adaption strategies that, relying on better information than in the past, support management and decision-making actions to plan adaptation strategies that enhance the resilience of urban water systems under uncertain future climate and social scenarios. The phenomenon of digitalization of urban water system will be analysed, starting from basic notions of modelling water distribution networks, and then adding more focus on Data Science and Artificial Intelligence approaches to modelling and controlling such networks. The course will be structured around the main topic of modelling and management of water distribution networks. In addition, other sub-topics will be touched during the course, enabling the students to get an overview of the different elements of modern urban water systems, acquire knowledge about best technologies, learn how to identify anomalies (e.g., leakages) in the normal operation of these systems, and get insights on the role and influence of human behaviours in such systems. The following CORE TOPICS will be covered: 1. Mathematical modelling and hydraulic modelling of water distribution networks (WDN) 2. Model calibration, validation, and performance assessment 3. Water demand modelling 4. Simulation and optimisation of WDN operations. In tackling the above topics, notions on the following DS and AI techniques will be introduced: 1. Time series analysis 2. Clustering techniques 3. Control theory and optimisation 4. Artificial neural networks. Additionally, one module of this course - the Journal Club - will be focused on developing skills for reading and analysing scientific literature. During the project activity, the students will be actively fostered to develop own solutions for a sample problems introduced during the guided practical lab activities. These activities will rely on open available datasets. Assessment includes final quizzes, a final presentation, and a short project report. The lecture will be given in English and will include lectures by international guest speakers.

Module Components


All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Data Science and Artificial Intelligence for Urban Water ManagementIntegrierte VeranstaltungSoSeEnglish4

Workload and Credit Points

Data Science and Artificial Intelligence for Urban Water Management (Integrierte Veranstaltung):

Workload descriptionMultiplierHoursTotal
Exam preparation7.08.0h56.0h
180.0h(~6 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

The lectures will be mainly in a frontal presentations format. Slides will be made available to students. The project includes tutoring sessions to guide the student through mathematical modelling tasks, starting from the basics of building a simple water distribution network in a virtual environment and moving on with more complex tasks, such as simulating and controlling the operation of such a network, and using Artificial Intelligence to model its main features. Guided activities will be complemented by open tasks, to be tackled by the students with guidance and feedback from the tutors. A short (max 5-7 pages) final report will be delivered at the end of the course, as prepared individually by each student. Further instructions about the final schedule, lecture rooms, and on how to get access to the lectures and exercise materials for the course will be communicated to the registered students via the e-learning ISIS platform.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Basic programming knowledge and previous experience with Python/Matlab/R is required. Guided practical activities will be performed in the course using Python and Jupyter Notebooks. Preferred competences (not compulsory): concepts of mathematical modelling, concepts of statistics and data analysis, and basic knowledge of water systems.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion



Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt



Test elements

Daily quizzes20written5 hours in total (cumulative of all quizzes)
Paper presentation30oral30 minutes (includes question time) + preparation time (max 3 days)
Project report50writtenEstimated duration for report preparation: 4 days maximum

Grading scale

Notenschlüssel »Notenschlüssel 4: Fak I, Fak VII«


Test description (Module completion)

Assessment includes: - daily quizzes, to be completed individually by each student after each lecture day; - a final paper presentation, to be prepared and presented in small groups - a short project report (approx. 5-7 pages), to be prepared individually, by each student.

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:

Maximum Number of Participants

The maximum capacity of students is 40.

Registration Procedures

Course registration via Prüfungsamt.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable


Electronical lecture notes

Availability:  available



Recommended literature
No recommended literature given

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
This module is not used in any degree program.

Students of other degrees can participate in this module without capacity testing.


No information