VM6011 - Hydroinformatics for Smart Water Systems


Examination arrangement

Examination arrangement: Oral examination and Work
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
work 20/100
work 20/100
Oral examination 60/100 E

Course content

Introduction to smart water systems:
Overview of opportunities and challenges
Examples from course participants: Practical problems they have encountered, and could they be solved using smart water systems?
Examples from municipality (Trondheim) and consulting companies (Norconsult/Multiconsult)
Introduction to modeling and platforms for communication and programming

Objective function
Neural networks
Genetic algorithms and multicriteria optimization
Machine learning

Uncertainty and multiobjective decisions analysis:
Probabilistic methods / Bayesian approach to uncertainty analysis, Monte-Carlo method
Decision making basics – attributes, weights, scores, …
Multi objective decision modelling

Smart water systems:
Pump selection and pumping station design
Smart pumping stations – from sensors to actuators
Digitalization of water infrastructure – applications in water and wastewater networks

Learning outcome

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

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 water systems

Overall competency
Students will develop a systems viewpoint to smart 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

Lectures, group discussions, exercises.
The course is part of NTNU's continuing education portfolio, and has a tuition fee. See our portal for continuing education, NTNU Videre.

Compulsory assignments

  • Exercises
  • Attendance

Further on evaluation

Two written reports, each counting 20 %. Oral exam including presentation of the written reports counting 60 %. You have to pass the reports to take the exam.

Specific conditions

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Admission to a programme of study is required:
Miscellaneous Courses - Faculty of Engineering (EMNE/IV)

Course materials

Information will be given at the start of the course.

More on the course



Version: 1
Credits:  7.5 SP
Study level: Further education, lower degree level


Term no.: 1
Teaching semester:  SPRING 2021

Language of instruction: English

Location: Trondheim

Subject area(s)
Contact information


Examination arrangement: Oral examination and Work

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Spring ORD work 20/100 INSPERA
Room Building Number of candidates
Spring ORD work 20/100 INSPERA
Room Building Number of candidates
Spring ORD Oral examination 60/100 E
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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