course-details-portlet

VM6011 - Hydroinformatics for Smart Water Systems

About

Examination arrangement

Examination arrangement: Oral examination and work
Grade: Letters

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

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

Optimization:

  • 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

Knowledge

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.

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 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

Compulsory activities from previous semester may be approved by the department.

Admission to a programme of study is required:
Continuing Education, Faculty of Engineering Science and Technology (EVUIVE0)
Miscellaneous Courses - Faculty of Engineering (EMNE/IV)

Course materials

Information will be given at the start of the course.

More on the course

No

Facts

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

Coursework

Term no.: 1
Teaching semester:  SPRING 2022

Language of instruction: English

Location: Trondheim

Subject area(s)
Contact information

Examination

Examination arrangement: Oral examination and work

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD Oral examination 60/100 E
Room Building Number of candidates
Spring ORD Work 20/100 INSPERA
Room Building Number of candidates
Spring ORD Work 20/100 INSPERA
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.
Examination

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

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