Course Details
Artificial inteligence methods in water management
Academic Year 2022/23
DSB026 course is part of 3 study plans
DKC-V Winter Semester 2nd year
DKA-V Winter Semester 2nd year
DPA-V Winter Semester 2nd year
Problems of uncertainty in rainfall-runoff modelling, stochastic processes, vague description of variables, adaptivity principle, learning systems, application of artificial neural networks, application of fuzzy models, application of genetic algorithms.
Credits
8 credits
Language of instruction
Czech
Semester
winter
Course Guarantor
Institute
Forms and criteria of assessment
examination
Entry Knowledge
Student gains basic knowledge of using artifical inteligence methods in water management problems solution
Aims
Application of basic methods of artificial inteligence in hydrology and water management
Syllabus
1. Problems of uncertainty in hydrology and water management.
2. Adaptivity principle and learning systems.
3.–4. Neural networks and their simulators.
5.–7. Application of neural networks on selected problems solutions.
8.–9. Fuzzy models.
10.–11. Application of fuzzy models.
12.–13. Genetic algorithms and their application.
2. Adaptivity principle and learning systems.
3.–4. Neural networks and their simulators.
5.–7. Application of neural networks on selected problems solutions.
8.–9. Fuzzy models.
10.–11. Application of fuzzy models.
12.–13. Genetic algorithms and their application.
Prerequisites
Hydrology, hydraulics, mathematics, probability theory and mathematical statistics, physics.
Specification of controlled instruction, the form of instruction, and the form of compensation of the absences
Extent and forms are specified by guarantor’s regulation updated for every academic year.
Offered to foreign students
Not to offer
Course on BUT site
Lecture
13 weeks, 3 hours/week, elective
Syllabus
1. Problems of uncertainty in hydrology and water management.
2. Adaptivity principle and learning systems.
3.–4. Neural networks and their simulators.
5.–7. Application of neural networks on selected problems solutions.
8.–9. Fuzzy models.
10.–11. Application of fuzzy models.
12.–13. Genetic algorithms and their application.
2. Adaptivity principle and learning systems.
3.–4. Neural networks and their simulators.
5.–7. Application of neural networks on selected problems solutions.
8.–9. Fuzzy models.
10.–11. Application of fuzzy models.
12.–13. Genetic algorithms and their application.