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SoSe 2020 - WiSe 2020/21

English

Data Science and Artificial Intelligence for Urban Water Management

6

Cominola, Andrea

Benotet

Portfolioprüfung

English

Zugehörigkeit


Fakultät V

Institut für Strömungsmechanik und Technische Akustik

35311100 FG Fluidsystemdynamik-Strömungstechnik der Maschinen und Anlagen

Physikalische Ingenieurwissenschaft

Kontakt


FSD

Cominola, Andrea

andrea.cominola@tu-berlin.de

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

Lernergebnisse

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), with guided practical activities. They will learn how to concisely present a research work.

Lehrinhalte

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 with 5 sub-topics, which will enable 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: 1. Introduction to Data Science and Artificial Intelligence in Urban Water Systems 2. Modelling and control of Urban Water Systems 3. Cybersecurity and other anomalies in urban water networks 4. Smart metering and behavioural modelling 5. 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 of leakage detection, where they will be guided to implement data-driven solutions on an open available dataset. Assessment includes a final oral exam, and a short project report. The lecture will be given in English and will include lectures by international guest professors.

Modulbestandteile

Compulsory area

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
Dieser Gruppe enthält keine Lehrveranstaltungen

Arbeitsaufwand und Leistungspunkte

Lehrveranstaltungsunabhängiger Aufwand:

AufwandbeschreibungMultiplikatorStundenGesamt
Attendance5.010.0h50.0h
Exam preparation1.028.0h28.0h
Exam1.02.0h2.0h
Pre/post processing10.010.0h100.0h
180.0h(~6 LP)
Der Aufwand des Moduls summiert sich zu 180.0 Stunden. Damit umfasst das Modul 6 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

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 pages) final report will be delivered at the end of the course. UPDATE COVID-19: Considering the current restrictions in place after COVID-19 outbreak, the Summer School is going fully virtual and students will not have to travel to Berlin and attend classroom activities - everything is going to be managed online. Further instructions will be communicated to registered students on how to get access to the online lectures and exercise materials for the school. The school is therefore still scheduled for May 25th - 29th 2020.

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

Preferred competences (not compulsory): concepts of mathematical modelling, basic programming knowledge, and basic knowledge of water systems.

Verpflichtende Voraussetzungen für die Modulprüfungsanmeldung:

Dieses Modul hat keine Prüfungsvoraussetzungen.

Abschluss des Moduls

Benotung

Benotet

Prüfungsform

Portfolio examination

Art der Portfolioprüfung

100 Punkte insgesamt

Sprache(n)

English

Prüfungselemente

NamePunkteKategorieDauer/Umfang
Oral exam1mündlichKeine Angabe
Project report1schriftlich28

Notenschlüssel

Notenschlüssel »Notenschlüssel 1: Fak IV (1)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt86.0pt82.0pt78.0pt74.0pt70.0pt66.0pt62.0pt58.0pt54.0pt50.0pt

Prüfungsbeschreibung (Abschluss des Moduls)

Assessment includes a final oral exam, and a short project report (approx. 5 pages).

Dauer des Moduls

Für Belegung und Abschluss des Moduls ist folgende Semesteranzahl veranschlagt:
1 Semester.

Dieses Modul kann in folgenden Semestern begonnen werden:
Winter- und Sommersemester.

Maximale teilnehmende Personen

Dieses Modul ist nicht auf eine Anzahl Studierender begrenzt.

Anmeldeformalitäten

Course registration via Prüfungsamt.

Literaturhinweise, Skripte

Skript in Papierform

Verfügbarkeit:  nicht verfügbar

 

Skript in elektronischer Form

Verfügbarkeit:  verfügbar

 

Literatur

Empfohlene Literatur
Keine empfohlene Literatur angegeben

Zugeordnete Studiengänge


Diese Modulversion wird in folgenden Studiengängen verwendet:

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Dieses Modul findet in keinem Studiengang Verwendung.

Studierende anderer Studiengänge können dieses Modul ohne Kapazitätsprüfung belegen.

Sonstiges

Keine Angabe