Course - Hydroinformatics for Smart Water Systems - VM6011
VM6011 - Hydroinformatics for Smart Water Systems
About
New from the academic year 2019/2020
Examination arrangement
Examination arrangement: Oral examination and Work
Grade: Letters
Evaluation | Weighting | Duration | Grade deviation | Examination aids |
---|---|---|---|---|
Arbeider | 20/100 | |||
Arbeider | 20/100 | |||
Home exam | 60/100 |
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
Admission to a programme of study is required:
Miscellaneous Courses - Faculty of Engineering (EMNE/IV)
Recommended previous knowledge
Bachelor
Course materials
Information will be given at the start of the course.
No
Version: 1
Credits:
7.5 SP
Study level: Further education, lower degree level
Term no.: 1
Teaching semester: SPRING 2020
Language of instruction: English
Location: Trondheim
Department with academic responsibility
Department of Civil and Environmental Engineering
Examination
Examination arrangement: Oral examination and Work
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
-
Spring
ORD
Home exam
60/100
Release
2020-05-07Submission
2020-05-07
09:00
INSPERA
10:30 -
Room Building Number of candidates - Spring ORD Arbeider 20/100
-
Room Building Number of candidates - Spring ORD Arbeider 20/100
-
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"