Course detail

Artificial inteligence methods in water management

FAST-DSB026Acad. year: 2022/2023

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.

Language of instruction

Czech

Number of ECTS credits

8

Mode of study

Not applicable.

Guarantor

prof. Ing. Miloš Starý, CSc.

Department

Institute of Landscape Water Management (VHK)

Learning outcomes of the course unit

Student gains basic knowledge of using artifical inteligence methods in water management problems solution

Prerequisites

Hydrology, hydraulics, mathematics, probability theory and mathematical statistics, physics.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Course curriculum

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.

Work placements

Not applicable.

Aims

Application of basic methods of artificial inteligence in hydrology and water management

Specification of controlled education, way of implementation and compensation for absences

Extent and forms are specified by guarantor’s regulation updated for every academic year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme DPC-V Doctoral 2 year of study, winter semester, compulsory-optional
  • Programme DKA-V Doctoral 2 year of study, winter semester, compulsory-optional
  • Programme DPA-V Doctoral 2 year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

39 hod., optionally

Teacher / Lecturer

prof. Ing. Miloš Starý, CSc.

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.