Moses - Data Science and Artificial Intelligence for Urban Water Management

Display language

Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SoSe 2021 - SoSe 2021. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#50905 / #2

Validity:
SoSe 2021 - SoSe 2021

Default display language:
English

Module title:
Data Science and Artificial Intelligence for Urban Water Management

Credits:
6

Responsible person:
Cominola, Andrea

Grading:
benotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät V

Institute:
Institut für Strömungsmechanik und Technische Akustik

Area of expertise:
35311100 FG Fluidsystemdynamik-Strömungstechnik der Maschinen und Anlagen

Prüfungsausschuss:
Physikalische Ingenieurwissenschaft

Kontakt

Office:
FSD

Contact person:
Cominola, Andrea

E-mail address:
andrea.cominola@tu-berlin.de

Website:

https://www.tu.berlin/en/swn

Learning Outcomes

During this course, the students will acquire knowledge about the latest advances in Data Science (DS) & Artificial Intelligence (AI) for modelling and managing urban water systems, with theory, methods, and applications. They will learn what the current research challenges in the field of urban water systems management are, with focus on the latest DS and AI technologies. They will approach the practical implementation of solutions to currently relevant problems in the fields of digitalisation of urban water and energy systems (e.g., leak detection in water distribution systems), with guided practical activities. They will learn how to concisely analyze and present a research work.

Content

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. In this course, the phenomenon of digitalization of urban water system will be analysed, with particular focus on Data Science and Artificial Intelligence approaches. The course will be structured around the main topic of modelling and management of water distribution networks and identification of anomalies (e.g., leakages) in their normal operation. 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, get insights on the interactions of water and energy systems in urban areas, and understand the role of human behaviour and cyber-physical security in such systems. The following 5 topics will be covered: MAIN TOPICS 1. Introduction to Data Science and Artificial Intelligence in Urban Water Systems 2. Modelling and control of Urban Water Systems 3. Leakage detection in Water Distribution Networks OTHER TOPICS 4. Cybersecurity and other anomalies in urban water networks 5. Smart metering and behavioural modelling 6. Water-energy nexus and water and urban development. During the project activity, the students will be actively fostered to develop own solutions for a sample problem (e.g., on leakage detection), where they will be guided to implement data-driven solutions on open available datasets. Assessment includes a final presentation combined with a short oral exam, and a short project report. The lecture will be given in English and will include lectures by international guest professors.

Module Components

Workload and Credit Points

Course-independent workload:

Workload description	Multiplier	Hours	Total
Attendance	5.0	10.0h	50.0h
Exam	1.0	2.0h	2.0h
Exam preparation	1.0	28.0h	28.0h
Pre/post processing	10.0	10.0h	100.0h
			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 the solution development process and give them feedback. A short (max 5-7 pages) final report will be delivered at the end of the course. Depending on the restrictions in place due to the COVID-19 outbreak, the course will be provided in a fully online or mixed online and in person format. Further instructions about the format, the dates, and on how to get access to the lectures and exercise materials for the course will be communicated to the registered students via mail or 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 Matlab/Python/R is required. Guided practical activities will be performed 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

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

Name	Points	Categorie	Duration/Extent
Oral exam	50	oral	No information
Project report	50	written	28

Grading scale

Notenschlüssel »Notenschlüssel 1: Fak IV (1)«

Gesamtpunktzahl	1.0	1.3	1.7	2.0	2.3	2.7	3.0	3.3	3.7	4.0
100.0pt	86.0pt	82.0pt	78.0pt	74.0pt	70.0pt	66.0pt	62.0pt	58.0pt	54.0pt	50.0pt

Test description (Module completion)

Assessment includes a final presentation combined with a short oral exam, and a short project report (approx. 5-7 pages).

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:
Winter- und Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 40.

Registration Procedures

Course registration via Prüfungsamt.

Recommended literature
No recommended literature given

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

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

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

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

Miscellaneous

No information

Data Science and Artificial Intelligence for Urban Water Management